| Issue |
E3S Web Conf.
Volume 710, 2026
54th AiCARR International Congress “Decarbonising our Future: Energy, Economic and Social Aspects of Smarter and Digitalized Buildings and Cities”
|
|
|---|---|---|
| Article Number | 04005 | |
| Number of page(s) | 13 | |
| Section | Digitalization and Smart Performance Management | |
| DOI | https://doi.org/10.1051/e3sconf/202671004005 | |
| Published online | 07 May 2026 | |
A New Experimentally Validated Archetype for Model Predictive Control of Residential Buildings with Heat Pump
1 Department of Theoretical and Applied Sciences, Università E-Campus, Novedrate 22060, Italy
2 Department of Mechanical Engineering, KU Leuven, 3001 Leuven, Belgium
3 EnergyVille, 3600 Genk, Belgium
4 Department of Industrial Engineering and Mathematical Sciences (DIISM), Università Politecnica delle Marche, Ancona, 60131, Italy
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Model Predictive Control (MPC) provides efficient control of building heating systems, integrating comfort and energy goals. However, widespread implementation remains constrained by the computational complexity of predictive models and the challenges of controlling advanced thermal distribution systems, particularly those with heat pumps. To address these limitations, the authors previously proposed several MPC archetypes that simplify design and enable large-scale use. Experimentally validated on a real heat pump system, they cover three heating setups, namely low-temperature radiators without storage, with integrated storage, and underfloor heating. The present study extends this framework by introducing a novel archetype specifically developed for heat pump systems with underfloor heating and an external thermal energy storage. The MPC minimizes an objective function configurable via a user-selected penalty signal, such as electricity price, primary energy consumption, or other operational criteria. This work presents the archetype and demonstrates its experimental validation using a Hardware-in-the-Loop setup, in which the building is emulated while the heat pump, thermal storage, and hydraulic circuits are physically implemented. The results demonstrate the accuracy and effectiveness of the MPC, with indoor temperature deviations remaining low (maximum 2.33 °Ch below and 4.40 °Ch above the set-point) and feasible solutions achieved in 98.52% of the test duration.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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