Modern smart grids increasingly incorporate distributed energy resources, electric vehicles and renewable generation, which are creating complex energy management challenges. Traditional control approaches lack the intelligence and responsiveness required to manage rapidly fluctuating loads and irregular renewable production. To address these limitations, this study proposes an AI-driven Energy Management System (EMS) designed to enhance grid stability, operational efficiency and energy optimization in smart grid environments.

The proposed EMS integrates a Long Short-Term Memory (LSTM) model for short-term load forecasting with a Reinforcement Learning (RL) controller for real-time operational decision-making. The LSTM model is trained using historical load demand data to generate accurate short-term predictions while the RL agent learns optimal coordination strategies for distributed resources such as battery storage systems and rooftop photovoltaic (PV) units. System performance was evaluated using multiple test scenarios reflecting variable consumer demand and fluctuating renewable energy availability.

Results demonstrate that the AI-based forecasting approach improves prediction accuracy by 22% compared with conventional statistical models. Intelligent battery scheduling achieved a 15% reduction in peak demand while renewable energy utilization increased by 26%. The system also exhibited strong adaptability to sudden load variations, improved voltage and frequency stability and enabled cost-effective operation under dynamic grid conditions.

These findings highlight the significant potential of AI-enabled energy management to increase the flexibility, efficiency and resilience of next-generation smart grids. The proposed framework presents a scalable pathway for managing complex distributed energy environments and supports the global transition toward more intelligent and sustainable energy systems.