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.

In this paper, we consider the problem of controller tuning for an operating unit in a building energy system. The illustrative example used here is a real heat pump located in the NEST building at Empa, Dubendorf, Zurich, with its outflow temperature controlled by a PI-controller. The plant is in use and accordingly, intervening in its normal operation is not allowed. Moreover, the model of plant is not available or it can be changed due to aging or possible modification. Accordingly, it is desired to utilize a tuning method which is model-free, operates online, does not intervene with the normal operation of the plant and use only the available historical measurement data. Additionally, it is required to guarantee the safety of the plant during the tuning procedure. In this regard, we formulate the controller tuning problem as a black-box optimization and adopt a safe Bayesian optimization approach for controller parameters tuning. In order to assess numerically the performances of the scheme, first we model the plant as a nonlinear ARX model in form of a feedforward neural network. Subsequently, we train the neural network using the available historical measurement data. Finally, the obtained model is used as an oracle in the controller tuning procedure in order to numerically verify the effectivity of the proposed approach.