Self-adjusting model predictive control for modular subsystems in HVAC systems

Abstract

In order to reduce the energy consumption and CO2 emissions in the building sector, an efficient control strategy, such as model predictive control (MPC) is required. However, MPC is rarely applied in buildings since the implementation and modeling is complex, time consuming and costly. To bring MPC into practice, controllers and models are needed, that automatically adapt their behavior to the controlled system. In this work, such a self-adjusting MPC applicable to heating, ventilation and air-conditioning (HVAC) systems is developed. The MPC is based on a simple grey-box model that is able to cover the general dynamics of the considered subsystem. The controller adapts the model parameters online according to the past measurements of the controlled system using a moving horizon estimation. The developed self-adjusting MPC is applied to three heating coils in a simulation. Compared with a PID controller, the self-adjusting MPC is able to increase the control quality up to 10%, while no manual tuning is needed. Additionally, the model predictive approach is able to reduce the power consumption of the pump by 80%.