AutoB2G：面向电网互动建筑控制的智能体仿真与强化学习框架
AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
AI 导读
AutoB2G 是一个电网感知的建筑—电网协同仿真环境，将 CityLearn V2 与 Pandapower 和 OpenDSS 集成，支持平衡及三相不平衡电网仿真和多种电网侧评估指标。
正文 · 原文
Abstract:Grid-interactive building control can improve demand-side flexibility, but realistic evaluation requires coordinated co-simulation of buildings, reinforcement learning (RL), and distribution grids. Translating user intent into executable co-simulation workflows is labor-intensive, requiring the manual integration and configuration of heterogeneous simulators and interfaces. We introduce AutoB2G, a grid-aware building--grid co-simulation environment integrating CityLearn V2 with Pandapower and OpenDSS. It supports balanced and three-phase unbalanced grid simulation and a broad range of grid-side evaluation metrics. AutoB2G organizes simulation components as a directed acyclic graph (DAG), explicitly encoding module dependencies and execution order for dependency-aware retrieval and composition. Building on this structure, we develop a large language model (LLM)-based agentic framework that automatically retrieves, composes, executes, verifies, and repairs simulation pipelines from natural-language task descriptions. Experiments on realistic distribution networks show improved reliability and executability of complex building--grid workflows, demonstrating the potential of agentic LLMs for scientific simulation orchestration in energy systems.
| Comments: | Accepted at NeurIPS 2026 Workshop on AI4PowerGrids |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.26005 [cs.AI] |
| (or arXiv:2603.26005v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.26005 arXiv-issued DOI via DataCite |
Submission history
From: Borui Zhang [view email]
[v1]
Fri, 27 Mar 2026 01:44:08 UTC (2,255 KB)
[v2]
Mon, 29 Jun 2026 05:25:17 UTC (2,404 KB)
[v3]
Sun, 25 Oct 2026 12:55:27 UTC (2,077 KB)
来源：arXiv cs.AI · arxiv.org