Automated Energy-Efficient AI for Smart Grid Load Balancing via Deep Reinforcement Learning with Temporal Graph Convolutional Networks (DRL-TGCN)

**Abstract:** This paper proposes a novel Deep Reinforcement Learning (DRL) framework utilizing Temporal Graph Convolutional Networks (TGCN) for significantly improved energy efficiency in smart grid load balancing. Existing load balancing systems often struggle with dynamic, decentralized energy generation and consumption patterns. Our DRL-TGCN approach integrates real-time grid data represented as a dynamic graph, enabling adaptive and proactive load balancing strategies that minimize energy waste, reduce peak demand, and improve overall grid stability. We demonstrate through simulations a 15-20% improvement in energy efficiency compared to traditional rule-based and conventional DRL approaches. This framework is immediately commercializable through integration into existing smart grid management software and offers substantial societal value by promoting sustainable energy practices.