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Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

Author

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  • Davide Fop

    (TEBE Research Group, BAEDA Lab, Department of Energy “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

  • Ali Reza Yaghoubi

    (TEBE Research Group, BAEDA Lab, Department of Energy “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

  • Alfonso Capozzoli

    (TEBE Research Group, BAEDA Lab, Department of Energy “Galileo Ferraris”, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

Abstract

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

Suggested Citation

  • Davide Fop & Ali Reza Yaghoubi & Alfonso Capozzoli, 2024. "Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator," Energies, MDPI, vol. 17(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5117-:d:1498929
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    References listed on IDEAS

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    1. Kuboth, Sebastian & Heberle, Florian & König-Haagen, Andreas & Brüggemann, Dieter, 2019. "Economic model predictive control of combined thermal and electric residential building energy systems," Applied Energy, Elsevier, vol. 240(C), pages 372-385.
    2. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    3. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
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