arXiv cs.AI· Keivan Faghih Niresi, Christian M{\o}ller Jensen, Carsten Skovmose Kalles{\o}e, Rafael Wisniewski, Olga Fink·· 9 天前AI 评分17
基于异构时空图神经网络的城市供热管网虚拟智能计量
Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks
AI 导读
研究提出异构时空图神经网络（HSTGNN）构建虚拟智能热量表，通过专用分支分别学习流量、温度和压力的图结构与时间动态，实现跨变量与空间关联的联合建模。该方法针对供热管网仪表稀疏、传感器故障频发的问题，在稀疏同步数据下显著优于现有基线。团队同时发布 Aalborg 智能水基础设施实验室采集的同步高分辨率数据集，以支持虚拟传感方法的系统比较。
正文 · 原文
Abstract:Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven control, predictive optimization, and early fault detection. Achieving these goals relies on sufficient observability, requiring continuous and well-distributed monitoring of thermal and hydraulic states. However, district heating systems are typically sparsely instrumented and frequently affected by sensor faults, limiting monitoring. Virtual sensing offers a cost-effective means to enhance observability, yet its development and validation remain limited in practice. Existing data-driven methods generally assume dense synchronized data, while analytical models rely on simplified hydraulic and thermal assumptions that may not adequately capture the behavior of heterogeneous network topologies. Consequently, modeling the coupled nonlinear dependencies between pressure, flow, and temperature under realistic operating conditions remains challenging. In addition, the lack of publicly available benchmark datasets hinders systematic comparison of virtual sensing approaches. To address these challenges, we propose a heterogeneous spatial-temporal graph neural network (HSTGNN) for constructing virtual smart heat meters. The model incorporates the functional relationships inherent in district heating networks and employs dedicated branches to learn graph structures and temporal dynamics for flow, temperature, and pressure measurements, thereby enabling the joint modeling of cross-variable and spatial correlations. To support further research, we introduce a controlled laboratory dataset collected at the Aalborg Smart Water Infrastructure Laboratory, providing synchronized high-resolution measurements representative of real operating conditions. Extensive experiments demonstrate that the proposed approach significantly outperforms existing baselines.
| Comments: | Accepted to Energy and Buildings |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2604.10166 [cs.LG] |
| (or arXiv:2604.10166v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.10166 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1016/j.enbuild.2026.118351
DOI(s) linking to related resources |
Submission history
From: Keivan Faghih Niresi [view email]
[v1]
Sat, 11 Apr 2026 11:27:06 UTC (1,960 KB)
[v2]
Thu, 8 Oct 2026 11:23:49 UTC (1,977 KB)
来源：arXiv cs.AI · arxiv.org