AI for Smart Grid Optimization: A Review of Machine Learning in Energy Efficiency and Sustainability

Abstract

Artificial intelligence is revolutionizing smart grids by improving energy distribution, demand forecasting, and grid security. This paper reviews AI-driven optimization techniques for power generation, load balancing, and fault detection. We analyze the role of deep learning in renewable energy integration, microgrid management, and real-time electricity pricing. Challenges such as data privacy, cybersecurity threats, and regulatory compliance are discussed. Future research directions highlight advancements in federated learning, AI-driven energy trading, and autonomous grid control.