MPC control for improving energy efficiency of a building air handler for multi-zone VAVs

The performance and energy saving of building heating, ventilation, and air conditioning (HVAC) systems can be significantly improved by the implementation of intelligent and optimal controls. This article presents a parametric modeling approach and a system-level control design to improve the energy efficiency of building HVAC systems. We present an auto-regressive moving average exogenous (ARMAX) model that relates the return air temperature and flow rate of an air-handling-unit (AHU) for multi-zone variable air volumes (VAVs). We also develop a model predictive control (MPC) to minimize the energy consumption of the AHU. The control tracks the set points subject to thermal load constraints from lower level VAVs. The optimal control can achieve over 27.8% energy saving on average as compared to the baseline control that is originally installed in the building, and can closely track the supply air flow rate and setpoint of room temperature. In this paper, all the data processing, model validation and implementation of the control algorithm are based on extensive measurements collected from an office building on the campus of the University of California, Merced.