AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control

arXiv:2603.26005v2 Announce Type: replace Abstract: Grid-interactive building control has emerged as a promising approach for improving demand-side flexibility in modern power systems. Realistic studies of such systems, however, require tightly coupled co-simulation across buildings, reinforcement learning (RL), and distribution grids to capture time-varying control dynamics over spatially distributed grid infrastructures. Constructing these workflows remains highly challenging in practice: researchers must coordinate heterogeneous simulators, configure grid environments, synchronize time-vary

Source: arXiv cs.AI — read the full report at the original publisher.

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