AutoB2G: A Large Language Model-Driven Agentic Framework For Automated Building-Grid Co-Simulation

ArXi:2603.26005v1 Announce Type: new The growing availability of building operational data motivates the use of reinforcement learning (RL), which can learn control policies directly from data and cope with the complexity and uncertainty of large-scale building clusters. However, most existing simulation environments prioritize building-side performance metrics and lack systematic evaluation of grid-level impacts, while their experimental workflows still rely heavily on manual configuration and substantial programming expertise.