SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations

District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced Distr...

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Bibliographic Details
Published inarXiv.org
Main Authors Jonne van Dreven, Cheddad, Abbas, Alawadi, Sadi, Ahmad Nauman Ghazi, Jad Al Koussa, Vanhoudt, Dirk
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.08.2024
Subjects
Anomalies
Clustering
District heating
Energy consumption
Fault detection
Fault diagnosis
Graph theory
Network topologies
Substations
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Summary:District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH network topology and allow for local anomaly detection without disclosing sensitive information, such as substation locations. The approach leverages a multi-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial neighborhood creation. Moreover, it introduces a merging technique that reduces noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD) and modified z-scores to flag anomalous substations. The results reveal that SHEDAD outperforms traditional clustering methods, achieving significantly lower intra-cluster variance and distance. Additionally, SHEDAD effectively isolates and identifies two distinct categories of anomalies: supply temperatures and substation performance. We identified 30 anomalous substations and reached a sensitivity of approximately 65\% and specificity of approximately 97\%. By focusing on this subset of poor-performing substations in the network, SHEDAD enables more targeted and effective maintenance interventions, which can reduce energy usage while optimizing network performance.
ISSN:2331-8422