Bayesian Change-Point Detection and Fault Scoring in District Heating Systems
Abstract
Smart heat meter data provide an important basis for monitoring district-heating installations, but fault reports are delayed, incomplete and temporally coarse: a report follows the appearance of a fault, sometimes by weeks, so the onset is unknown. This blurs the distinction between statistical changes and operational faults and prevents the reliable labelling that fault classifiers require. Changepoint detectors such as the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) report only the probability that a change occurred, so a large jump and a much smaller one can receive the same probability, and probabilities alone cannot tell a utility which installation to inspect first. We therefore develop a decision score that applies BEAST to temperature difference and flow and scores each detected changepoint by its posterior probability together with its deviation from recent operation, jump size, local behaviour and physical direction. Cases with unfavourable changes in both channels, the pattern of insufficient cooling, receive the highest priority. Of 1,522 cases, 648 had a reported fault; these made up 96% of the 25 highest-priority cases and 90% of the highest 200, and Average Precision was 0.771 against a reported-fault prevalence of 0.426. As cases without a report are not verified to be fault-free, these figures are likely conservative. Without using the report date, the strongest unfavourable flow change in reported-fault cases fell a median of 11 days before the report. Level-based screening of temperature difference, as practised by utilities, ranked the cases comparably but cannot date the change, and the two kinds of evidence were complementary. The method can serve as the first step in a fault-detection workflow, providing a manageable subset of installations for inspection and candidate onset dates for training fault classifiers.
Related
- Optimisation of distributed storage in existing district heating networks to solve hydraulic bottlenecks
- A MINLP optimisation method for the integration of storage in district heating networks
- Risk-Aware Unit Commitment in District Heating Networks: Chance-Constrained and CVaR Optimization Under Demand Uncertainty
- Variational Bayesian Data Detection for Multiuser MIMO Systems Corrupted by Phase Noises
- Model Predictive Control for Dynamic Hydraulic Balancing in Building Radiator Heating Systems: Modeling, Design, and Experimental Validation
BibTeX
@misc{bugajski2026bayesian,
title = {{Bayesian Change-Point Detection and Fault Scoring in District Heating Systems}},
author = {Tanja Korsten Bugajski and Nicolai Peder Bülow Pedersen and J. Eduardo Vera-Valdés and Michal Zbigniew Pomianowski and Anna Marszal-Pomianowska},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04682},
eprint = {2611.04682},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04682},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bayesian Change-Point Detection and Fault Scoring in District Heating Systems
AU - Tanja Korsten Bugajski
AU - Nicolai Peder Bülow Pedersen
AU - J. Eduardo Vera-Valdés
AU - Michal Zbigniew Pomianowski
AU - Anna Marszal-Pomianowska
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04682
UR - https://arxiv.org/abs/2611.04682
AB - Smart heat meter data provide an important basis for monitoring district-heating installations, but fault reports are delayed, incomplete and temporally coarse: a report follows the appearance of a fault, sometimes by weeks, so the onset is unknown. This blurs the distinction between statistical changes and operational faults and prevents the reliable labelling that fault classifiers require. Changepoint detectors such as the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) report only the probability that a change occurred, so a large jump and a much smaller one can receive the same probability, and probabilities alone cannot tell a utility which installation to inspect first. We therefore develop a decision score that applies BEAST to temperature difference and flow and scores each detected changepoint by its posterior probability together with its deviation from recent operation, jump size, local behaviour and physical direction. Cases with unfavourable changes in both channels, the pattern of insufficient cooling, receive the highest priority. Of 1,522 cases, 648 had a reported fault; these made up 96% of the 25 highest-priority cases and 90% of the highest 200, and Average Precision was 0.771 against a reported-fault prevalence of 0.426. As cases without a report are not verified to be fault-free, these figures are likely conservative. Without using the report date, the strongest unfavourable flow change in reported-fault cases fell a median of 11 days before the report. Level-based screening of temperature difference, as practised by utilities, ranked the cases comparably but cannot date the change, and the two kinds of evidence were complementary. The method can serve as the first step in a fault-detection workflow, providing a manageable subset of installations for inspection and candidate onset dates for training fault classifiers.
ER -