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Is it returning too hot? Time series segmentation and feature clustering of end-user substation faults in district heating systems

Abstract

This study explores the challenges and advancements in collecting ground-truth data to enhance fault diagnosis models for district heating systems. Initiated by the need to address limitations in previous data collections, this research leverages an enriched dataset from a Danish district heating utility to identify faults in household substations. Despite some inaccurate fault categorizations, complex fault patterns, and truncated measurements, the analysis of 50 detailed cases out of 127 fault reports reveals that, while return temperature reliably indicates faults, energy usage patterns do not. By employing self-organizing maps combined with k-means clustering, fault symptoms and patterns were categorized adequately, demonstrating the utility of high-dimensional data clustering in fault diagnosis. Additionally, an algorithm using time series decomposition is suggested to identify extreme and subtle anomalies, enhancing fault detection capabilities. The paper concludes that these methodologies significantly improve the accuracy and dependability of fault diagnostics in district heating systems, paving the way for more efficient operational management.
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Category

Academic article

Language

English

Author(s)

  • Daniel Leiria
  • Hicham Johra
  • Justus Anoruo
  • Imants Praulins
  • Marco Savino Piscitelli
  • Alfonso Capozzoli
  • Anna Marszal-Pomianowska
  • Michal Zbigniew Pomianowski

Affiliation

  • SINTEF Community / Architectural Engineering
  • Aalborg University
  • Politecnico di Torino

Year

2025

Published in

Applied Energy

ISSN

0306-2619

Volume

381

View this publication at Norwegian Research Information Repository