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arXiv AI By Keivan Faghih Niresi, Christian M{\o}ller Jensen, Carsten Skovmose Kalles{\o}e, Rafael Wisniewski, Olga Fink

Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks

Read the original on arXiv AI →

The paper proposes a heterogeneous spatial‑temporal graph neural network (HSTGNN) to create virtual smart heat meters for district heating networks, addressing sparse instrumentation and sensor faults. It integrates network functional relationships and learns graph structures and temporal dynamics for flow, temperature, and pressure, enabling joint modeling of cross‑variable and spatial correlations. A new laboratory dataset from the Aalborg Smart Water Infrastructure Laboratory is introduced, and experiments show the HSTGNN outperforms existing baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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