Indoor air quality control of HVAC system

Abstract

Reliable and optimal monitoring and control of ventilation system are essential for a heating, ventilation and air conditioning (HVAC) system to maintain adequate indoor air quality with least energy consumption. This paper presents the development and validation of a control algorithm that adapts to the dynamics of a HVAC system using sensor-based demand-controlled ventilation. The control strategy, which is based on monitoring and modelling of indoor carbon dioxide (CO 2 ) concentration, is employed to respond to the changes of indoor CO 2 generation through appropriate adjustment of ventilation rates, i.e., the rate of ventilation is modulated over time based on the signals from indoor CO 2 concentration. In particular, the paper focuses on the development of adaptive indoor air quality model based on soft real-time indoor occupant prediction for implementing control strategies. The results show that our model is capable of predicting the indoor CO 2 of a dynamic indoor environment. This dynamic indoor air quality model is useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems.