Commissioning oriented fault detection in heat pump operation using machine learning algorithms
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
HPC 2026
Themenheft
DOI der Originalpublikation
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
Verlagsort / Veranstaltungsort
Vienna
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Heat pumps now play a significant role in Europe’s residential heating sector. As deployment continues to expand, there is an increasing need for scalable and commissioning-friendly fault detection (FD) solutions that operate with minimal instrumentation and limited historical data. Existing machine-learning-based FD approaches rely heavily on long process histories or simulation data and require extensive expert configuration, making them unsuitable for early-stage deployment. This paper proposes a lightweight, data-driven FD framework based on short-horizon time-series forecasting using autoregressive–exogenous (ARX) models. The method requires only three consecutive no-fault days for training and incorporates a daily re-initialization loop to mitigate cumulative prediction drift. Deviations between predicted and measured flow-temperature profiles—selected as the primary diagnostic variable due to its universal availability and strong sensitivity to common fault modes—are evaluated using a composite set of complementary residual metrics, including root mean squared error, maximum absolute error, and correlation-based pattern matching.
The framework was tested on seven real-world datasets, with detailed results presented for two representative heat-pump systems: a ground-source heat pump (GSHP) and an air-to-water system. Across both cases, the ARX model achieved strong forecasting accuracy and reliably identified deviations indicative of abnormal behavior, while requiring minimal configuration effort. These results demonstrate the potential of lightweight forecasting-based FD methods as practical commissioning tools and motivate further work toward hybrid approaches that integrate rule-based and data-driven diagnostics for broader real-world deployment.
Schlagwörter
Energy informatics, Fault-detection, Time-series forecasting
Fachgebiet (DDC)
Veranstaltung
15th IEA Heat Pump Conference
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
26.05.2026
Enddatum der Konferenz
29.05.2026
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Lizenz
Zitation
Sawant, P., & von Bülow-Köster, N. (2026). Commissioning oriented fault detection in heat pump operation using machine learning algorithms. Hpc 2026. 15th IEA Heat Pump Conference. https://irf.fhnw.ch/handle/11645/57964