AI-Driven Predictive Energy Management for Smart Renewable Power Grids: Techniques, Challenges, and Future Directions

Abstract

The rapid integration of intermittent renewable energy sources (RES), such as solar photovoltaics and wind turbines, into modern electrical grids presents operational challenges regarding voltage stability, frequency regulation, and real-time supply-demand balancing. Traditional deterministic energy management systems (EMS) lack the dynamic adaptation and predictive foresight necessary to efficiently mitigate the stochastic nature of renewable generation and volatile load dynamics. Artificial Intelligence (AI), encompassing Deep Learning (DL), Machine Learning (ML), and Reinforcement Learning (RL), has emerged as a transformative driver for Predictive Energy Management Systems (PEMS) in smart renewable grids. This review paper provides an in-depth technical analysis of state-of-the-art AI techniques applied to load and renewable power forecasting, optimal energy dispatch, battery energy storage system (BESS) management, and demand response (DR) optimization. We evaluate various machine learning architectures—including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Transformers, Extreme Gradient Boosting (XGBoost), and Deep Q-Networks (DQN)—across forecast accuracy, computational overhead, and grid stability. Furthermore, this study identifies major technical bottlenecks, including telemetry latency, cybersecurity vulnerabilities, model interpretability, and system scalability. Finally, strategic future research directions are delineated, emphasizing physics-informed neural networks (PINNs), federated edge learning, and quantum-inspired AI for resilient smart power infrastructures.

KEYWORDS: Smart Renewable Power Grids, Predictive Energy Management Systems (PEMS), Deep Reinforcement Learning, Load

Forecasting, Battery Storage Optimization, Grid Resilience.