Commissioning oriented fault detection in heat pump operation using machine learning algorithms
Loading...
Author (Corporation)
Publication date
2026
Type of student thesis
Course of study
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
HPC 2026
Special issue
DOI of the original publication
Related research data
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Place of publication / Event location
Vienna
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
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.
Keywords
Energy informatics, Fault-detection, Time-series forecasting
Subject (DDC)
Event
15th IEA Heat Pump Conference
Exhibition start date
Exhibition end date
Conference start date
26.05.2026
Conference end date
29.05.2026
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
License
Citation
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