Controller Tuning by Bayesian Optimization An Application to a Heat Pump

In this paper, we consider the problem of controller tuning for an operating unit in a building energy system. As an illustrative plant example we focus on a heat pump. Since the plant is in use, the tuning method is supposed to not intervene with its operation. Moreover, the tuning procedure is supposed to be online, model-free, based only on historical data and needs to provide safety guarantees of the plant in operation. In this regard, we formulate the problem as a black-box optimization and adopt safe Bayesian optimization approaches for controller parameter tuning. These approaches are relatively new to the control community and not intensively studied in control applications. Meanwhile, the underlying systems are often expensive and performing relevant experiments is time consuming. Therefore, a crucial step prior to implementation in reality is validating the methods in simulation to verify their applicability. Toward this end, we derive a physical-based model for the heat pump and identify the unknown parameters using gray-box identification methods. Given the simulation model, we tune the controller parameters in simulation for optimal performance while considering safety constraints of the system.