Approximating model predictive control strategies for heat pump systems applied to the building optimization testing framework (BOPTEST)

Abstract

Model predictive control (MPC) is promising for optimizing building's operation but high hardware, software and know-how requirements impede its commercialization. Therefore, rule-based controllers (RBC) are state-of-the-art. Approximate MPC (AMPC) can help bridge this gap by replacing the optimization with an explicit functional relation called approximator. Literature lacks reproducible use cases and benchmarks and a comparison of sophisticated and traditional approximators. This study aims to close this gap by applying AMPC to BOPTEST's two-zone heat pump testcase. The BOPTEST testcase includes predefined RBCs and KPIs promoting repeatability. Then, a comparison was made between artificial neural networks (ANNs), random forest (RF), linear, and logistic regression. The results show that feature selection significantly affects the performance. After adapting the features, the ANNs and RF outperform the RBC with cost savings of up to 33% and discomfort reductions of 70%, while requiring 15% of the MPC's computation time. The traditional approximators fail to outperform the RBC.