Deep Learning-Based Predictive Control for Renewable-Integrated Smart Grids: A Real-Time Performance Analysis

The increasing penetration of renewable energy resources has significantly improved the sustainability of modern power systems but has also introduced operational uncertainty and instability. Traditional control mechanisms often fail to respond effectively to rapid fluctuations in solar and wind generation. This paper proposes a deep learning–based predictive control framework that integrates Long Short-Term Memory (LSTM) forecasting with Model Predictive Control (MPC) to enhance the real-time performance of renewable-integrated smart grids. LSTM networks are utilized to forecast short-term renewable output and load profiles, while the MPC layer optimally adjusts inverter setpoints, voltage regulators, and demand response signals. Simulation results demonstrate that the proposed approach reduces voltage deviation by up to 56%, improves renewable utilization by 18%, and decreases operational cost by 22% compared to conventional control strategies. These outcomes validate the capability of deep learning models to support intelligent, adaptive, and real-time grid management.