Model predictive control for thermal comfort and energy optimization of an air handling unit system in airport terminals using occupant feedback

Abstract

The dynamic fluctuations in occupant flow within airport terminals contribute to delays in the system's response under traditional feedback control. This imbalance between supply and demand can result in discomfort and unnecessary energy consumption. While Occupant-Based Model Predictive Control (OBMPC) has gained research interest as it leverages occupant forecasts to enhance control performance, there is limited knowledge regarding its application in the air-conditioning systems of airport terminals, mainly due to the intricate dynamics of occupant flow in terminal buildings. In this study, we proposed a model predictive control strategy based on dynamic occupant flow with the goal of fast responding to occupant flow and reducing energy consumption. We integrated mathematical formulas and the Anylogic simulation software to predict occupant variations in the baggage claim hall. The model predictive control strategy of the air conditioning system was proposed with the predicted occupant flow. The proposed strategy was evaluated throughout the whole cooling season in the validated simulation model of the air handling unit system of the baggage claim hall at an airport terminal. The results demonstrated that the proposed strategy could effectively respond to variations in occupancy and achieve 10% energy savings throughout the entire cooling season, compared to conventional feedback control. Our work presents a viable solution to address system regulation lag and reduce energy consumption without jeopardizing thermal comfort.