HEAT: Hierarchical-constrained Encoder-Assisted Time series clustering for fault detection in district heating substations

Abstract

Fault detection in district heating (DH) substations is crucial for maintaining energy efficiency. However, existing methods often fall short and rely on labelled data or global analysis that may miss subtle anomalies. We introduce HEAT, a Hierarchical-constrained Encoder-Assisted Time series clustering method designed to enhance fault detection in DH substations. HEAT operates in a two-phase approach: first, it approximates a relative network topology using a constraint hierarchical clustering algorithm on supply temperature profiles. HEAT incorporates a Convolutional AutoEncoder (CAE) for dimensionality reduction of the time series data and uses adaptive soft constraints in the linkage function, enabling both minimum and maximum cluster size constraints while supporting domain knowledge, e.g., must-link and cannot-link constraints, using a constraint matrix. Second, we use the topology approximation to perform intra-cluster analysis using Mean Absolute Deviation (MAD) z-scores, with neighbouring substations serving as a validation mechanism, allowing for robust analysis without requiring labelled data. Experimental results demonstrate that HEAT outperforms conventional clustering methods while achieving 74.1% sensitivity and 95.5% specificity in fault detection, significantly improving over typical global analysis. HEAT not only identified major faults (e.g., sensor issues, valve failures) but also detected subtle anomalies (e.g., secondary leakages) while minimising false positives. This unsupervised method offers a viable and flexible solution for DH networks, improving operational efficiency and energy sustainability without disclosing sensitive information.