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Bayesian Change-Point Detection and Fault Scoring in District Heating Systems — arXiv:2611.04682

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04682
License (as archived): CC0-1.0
Archived: 2026-11-04T21:54:51.816Z
SHA-256 of the archived text: 9067d077c13e3ef14149046a058d96f5cbba258aa5dc3ccfa6075b2452d1050d
Stable URL of this page: https://metraipolis.com/documento/dc_0806a2eeda10

How to cite

Bayesian Change-Point Detection and Fault Scoring in District Heating Systems — arXiv:2611.04682 — arXiv (en), https://arxiv.org/abs/2611.04682 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0806a2eeda10 (Metraipolis archive dc_0806a2eeda10, sha256 9067d077c13e3ef14149046a058d96f5cbba258aa5dc3ccfa6075b2452d1050d)

BibTeX

@misc{dc0806a2eeda10,
  title = {Bayesian Change-Point Detection and Fault Scoring in District Heating Systems — arXiv:2611.04682},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04682},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0806a2eeda10; sha256 9067d077c13e3ef14149046a058d96f5cbba258aa5dc3ccfa6075b2452d1050d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04682. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Applications (stat.AP) ; Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2611.04682 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T21:39:49.522Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).