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Adaptive demand-based control of heat pumps in non-residential buildings using supervised machine learning
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DOI：10.1016/j.segy.2026.100242.png)
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Abstract
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• Supervised learning predicts absolute supply-water temperature setpoints from BMS signals. • A rule-based supervisor ensures safe and stable operation. • Models are validated in 2 buildings in different climates and system architectures. • Field deployment in a hybrid HP–DH plant increased COP by 15.8% and doubled heat-pump share. • Plug-and-play integration via baseline-plus-correction requires no new hardware.
Keywords:
Heat pumps
Building energy systems
Machine-learning control
Rule-based supervision
Demand-based control
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