Optimizing Renewable Energy Integration & Grid Efficiency using AI & Reinforcement Learning
This project focuses on intelligent energy management for smart grids by integrating renewable energy sources, battery storage systems, and AI-driven optimization techniques. Using Deep Q-Learning (DQL) for decision-making, the system balances grid import, renewable generation, and storage utilization to minimize costs and enhance efficiency.
Deep Q-Learning for Grid Optimization – Reduces reliance on non-renewable sources.
Renewable Energy Forecasting – Predicts solar and wind power availability.
Battery Storage Management – Optimizes charge/discharge cycles for efficiency.
Dynamic Grid Load Balancing – Reduces energy wastage and stabilizes supply-demand.
Real-Time Visualization & Insights – Interactive dashboards for monitoring grid performance.