Optimization of renewable energy management using machine learning and genetic algorithms

This study aims to develop an optimized Energy Management System (EMS) that effectively integrates photovoltaic (PV) energy and load forecasting with intelligent control strategies to reduce energy costs and grid dependency. The increasing integration of intermittent renewable energy sources, particularly solar power, into energy systems poses challenges in maintaining the supply demand balance and minimizing energy waste. Without advanced control, substantial portions of the generated energy are lost and the energy demand during peak hours remains unmet. This study employs machine learning models—Random Forest for PV forecasting and Linear Regression for load prediction—to enable accurate energy forecasting. These forecasts are embedded into a GA-optimized EMS framework that dynamically manages battery usage, load shifting, and grid interaction. Simulations were conducted to evaluate the system performance under real-world conditions. The optimized EMS reduced the grid dependency by 39.29% and decreased the energy costs by 35.14%. It effectively utilized surplus PV energy, minimized energy waste, and improved the battery and grid coordination. Without the EMS, 40% of the energy is wasted, and 45.45% of the demand remains unmet during peak periods. The results demonstrated the potential of intelligent EMS frameworks to enhance energy efficiency, ensure supply reliability, and support sustainability goals. The model is scalable and adaptable to various energy environments, including smart grids and decentralized systems. By integrating accurate forecasting with GA-based control, the proposed EMS offers a practical and cost-effective solution for optimizing renewable energy management, reducing reliance on non-renewable sources, and promoting energy self-sufficiency.