Data-driven model predictive control for building climate control: Three case studies on different buildings

Abstract

Model predictive control(MPC) achieves great performance in energy management of the building. However, identifying a suitable control-oriented model for MPC is a challenging task. To overcome this problem, we attempt to apply the data-driven models which have universal approximation ability to the MPC task. In this paper, we propose a hybrid optimization algorithm, namely BSAS-LM algorithm, to solve the optimization problem with non-linear or non-convex data-driven models involved in data-driven predictive control(DDPC). To demonstrate the feasibility and scalability of the proposed hybrid optimization method, three case studies are implemented in three buildings with different geometries. The DDPC controllers are developed for each case study in three scenarios, namely constant temperature setpoint, lower temperature setpoint and pre-heating. EnergyPlus is employed to develop the building models and is then exported to Functional Mock-up Units(FMUs) for co-simulation. In the case study #1, the data-driven algorithms such as auto-regressive with external disturbance (ARX) and support vector regression(SVR) are used to develop models for a single-zone building. Those models are then applied in DDPC for climate control of the building. In the case study #2 and #3, the multilayer perceptron(MLP)-based DDPC is applied to two three-zones buildings. Results show that DDPC achieves comparable performance to the grey-box model based MPC. Besides, results also demonstrate the feasibility and scalability of the proposed method in DDPC integrated with various data-driven models.