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

TL;DR

SHEDAD is a novel anomaly detection method for district heating systems that uses a privacy-preserving graph approach to identify faulty substations, improving maintenance and energy efficiency.

Contribution

The paper introduces SHEDAD, a new SNN-enhanced anomaly detection technique that approximates network topology without revealing sensitive data, outperforming traditional clustering methods.

Findings

Identified 30 anomalous substations with 65% sensitivity and 97% specificity.

Outperformed traditional clustering methods in intra-cluster variance and distance.

Effectively distinguished anomalies in supply temperatures and substation performance.

Abstract

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.