Review of modeling methods for HVAC systems

This work presents the literature review of the methods used to model the heating, ventilation, and air conditioning (HVAC) systems. The model development is necessary for the study of the energy consumption of HVAC systems. Models are also required to simulate the different supervisory and local loop control strategies to improve the energy consumption efficiency. HVAC systems have complex structures consisting of heat and mass transfer equipment such as chiller, boiler, heating/cooling coils, and supply air ducts. HVAC systems also consist of several sensors and controllers for regulating the controllable variables such as zone temperature, supply air temperature, supply air fan speed, duct static pressure, and chilled water temperature at their set-points. To predict the energy consumption by the HVAC systems accurately, one needs to model the individual components either from the measured data or based on the knowledge of the underlying physical phenomenon. This results in three broad classes of the models known as data driven, physics based, and grey box models. In this paper, major data driven, physics based, and grey box modeling techniques reported in the recent literature are reviewed.