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
The increasing integration of AI, especially agentic systems and reinforcement learning, with critical infrastructure like power grids is a rapidly developing area of research and practical application.
Advanced control systems for grid-interactive buildings are crucial for demand-side flexibility, grid stability, and energy efficiency, directly impacting resource management and infrastructure resilience.
The development of agentic simulation and reinforcement learning tools will enable more sophisticated and autonomous management of building energy consumption in relation to grid demands.
- · Smart building technology providers
- · Energy management software companies
- · Power grid operators
- · AI/ML research institutions
- · Traditional building control system manufacturers (slow to adapt)
- · Inefficient power generators (less predictable demand)
More efficient and responsive power grids capable of handling fluctuating renewable energy sources.
Reduced energy waste and lower carbon emissions through optimized consumption patterns in buildings.
Enhanced resilience of critical infrastructure against cyber-attacks or natural disasters through distributed intelligent control.
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Read at arXiv cs.AI