arXiv：cs.LG· Shiliang Zhang, Sabita Maharjan, Alexey Vinel, Shui Yu·· 13 小时前
基于开放数据的城市电网拓扑与层级识别
Urban Power Grid Topology and Hierarchy Identification from Open Data
AI 导读
研究提出一套开放数据驱动的框架，可从高压输电网一直还原到单栋建筑接入的完整城市电网拓扑。该框架整合电力线、变电站、变压器、电线杆等公共基础设施数据勾画高中压电网骨架，再用地理空间机器学习对 OpenStreetMap 建筑数据做能耗负荷聚类，并据此推导末端配电线路的物理拓扑。框架已在挪威奥斯陆 Alna 区和新西兰 Waiheke 岛应用，重建了连接全部建筑与电力资产的完整电网拓扑。
正文
Abstract:Understanding the complex topology and hierarchy of urban power grid is crucial for energy prognosis, power flow management, and system resilience analysis. However, detailed grid information remains largely proprietary. This information, nevertheless, is crucial especially for analyzing the last-mile distribution networks connecting individual buildings. This paper addresses this challenge by developing an open-data-driven framework to identify urban power grid topology, from high-voltage transmission down to individual building connections. We integrate public infrastructure data (power-lines, substations, transformers, poles) to map the high and medium-voltage grid skeleton. We then leverage geospatial machine learning on OpenStreetMap building data to cluster loads of building energy demand. Upon the clustering, we develop the physical topology of the final distribution lines linking the clustered buildings and low-voltage distribution grids. Our derived grid topology incorporates physical and geographical constraints like voltage level and road networks, thus leading to reasonable topology and hierarchy of grid networks. We apply the developed framework to the districts of Alna in Oslo, Norway, and Waiheke Island in New Zealand, and we reconstruct the complete grid topology that connects all the buildings, power lines, and electricity infrastructure assets. With the developed framework, we provide a critical tool that facilitates power system analysis, e.g., power flow optimization, cascading failure simulation, and grid resilience against the penetration of distributed renewable generation. This work also opens the opportunity to build energy digital twins that fuse power assets and grid topology, geographic constraints, and fine-grained power demand profiles.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.21352 [cs.LG] |
| (or arXiv:2606.21352v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.21352 arXiv-issued DOI via DataCite |
Submission history
From: Shiliang Zhang [view email]
[v1]
Fri, 19 Jun 2026 11:51:38 UTC (12,892 KB)
[v2]
Fri, 9 Oct 2026 13:49:37 UTC (13,449 KB)
来源：arXiv：cs.LG · arxiv.org