Multi-objective model predictive control for air-source heat pumps: Leveraging system flexibility to simultaneously reduce noise and increase efficiency

Applied Thermal Engineering, vol. 278, pp. 126926

Abstract

Air-source heat pumps (ASHPs) are pivotal to Europe’s transition to decarbonized residential heating. To expand their market reach, improving energy efficiency and reducing noise emissions are essential. Both factors are largely influenced by the compressor and fan speeds, which are managed by the heat pump controller. While advanced control algorithms, such as model predictive control (MPC), have shown promise in enhancing energy efficiency by leveraging system flexibility, their potential for reducing noise emissions remains largely unexplored. This paper presents a multi-objective data-driven MPC using a weighted cost function approach that concurrently improves energy efficiency and reduces noise emissions. We employ a building energy system simulation with an energetically and acoustically calibrated heat pump model and evaluate two MPC strategies: one optimizes efficiency and the other prioritizes noise reduction. We evaluate the robustness of the MPC configurations across two placement scenarios, that introduce variations in noise immission. Compared to a reference controller, the Efficiency-MPC achieves a 10% S C O P improvement throughout a heating season while reducing acoustic discomfort by up to 20%. The Acoustic-MPC increases energy efficiency by up to 6% and significantly reduces acoustic discomfort by up to 92%. The Acoustic-MPC leverages system flexibility by shifting noise emissions to daytime periods, where higher acoustic limits are allowed. Further, our results show that the MPC’s cost function weights remain stable despite changes in heat pump placement. Retuning the weights for the second placement scenario reduces acoustic discomfort by 4%, suggesting that the MPC configurations can be applied effectively to different scenarios without requiring further adjustments. These results underscore data-driven MPC as a promising tool for optimizing ASHP operation in terms of both efficiency and acoustics.

Authors 4

1. Jonas Klingebiel Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, Germany

2. Moritz Beckschulte corresponding Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, Germany

3. Christian Vering Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, Germany

4. Dirk Müller Aachen E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate, Mathieustraße 10, Aachen, 52074, Germany