Prototype of a commissioning-friendly fault detection tool for residential heat pumps using machine learning algorithms
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2026
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04B - Conference paper
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Bern
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Abstract
Heat pumps are central to residential decarbonization, yet undetected operational faults can reduce system efficiency by up to 25%. Existing fault detection methods rely on extensive labeled data, detailed physical models, or specialist configuration — none of which are available immediately after commissioning. This article presents a lightweight, commissioning-friendly fault detection framework based on short-horizon time-series forecasting using a regularized ARX model. Trained on as few as three fault-free reference days, the framework evaluates daily deviations between predicted and measured flow temperature using three complementary residual metrics combined through a logical OR rule. Evaluated on 12 real-world residential heat pump datasets and one synthetic dataset across air-to-water, hybrid, and ground-source configurations, the framework achieved zero missed faults in eight of twelve systems with a mean false positive rate of 3.9 days per system.
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32. Tagung des Forschungsprogramms Wärmepumpen und Kältetechnik des Bundesamts für Energie BFE
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24.06.2026
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24.06.2026
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English
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Yes
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Zero Emission
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Published
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not peer-reviewed
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Green
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Citation
Sawant, P., & von Bülow-Köster, N. (2026). Prototype of a commissioning-friendly fault detection tool for residential heat pumps using machine learning algorithms (Bundesamt für Energie BFE, Ed.). https://doi.org/10.26041/fhnw-17220