APPLICATIONS AI-DRIVEN SOLAR ENERGY MANAGEMENT SYSTEM FOR SMART GRIDS USING PREDICTIVE ANALYTICS AND ADAPTIVE CONTROL

Power distribution that is smart, sustainable, and efficient is the result of a new generation of energy networks that use cutting-edge technology. Solar energy management systems utilizing AI can mitigate the effects of renewable power generation intermittency and fluctuations in energy demand to improve smart grid operational efficiency. The paper proposes SEMS-PA2C, an artificial intelligence (AI) powered solar energy management system (SEMS) for smart grids that employs adaptive control (AC) and predictive analytics (PA) to enhance energy sustainability and reliability. The SEMS-PA2C uses weather and past solar generation data to train prediction models using Gradient Boosting and Long Short-Term Memory (LSTM) networks. Adaptive control uses Reinforcement Learning (RL) to optimize energy distribution by balancing grid needs with battery storage utilization. The system is evaluated by running simulations on smart grid datasets incorporating real-world solar energy metrics and grid load profiles. According to major findings, the approach enhances solar energy utilization by 20% and reduces grid dependency by 15% compared to typical control systems. The adaptive control system also reduced energy losses during peak hours by 10%, which enhanced grid stability. According to the study's findings, a scalable solution to the challenge of developing sustainable power grids is smart grids that integrate adaptive control with predictive analytics to manage solar energy efficiently.