Simulation Study of Infiltration Effects on Demand Controlled Ventilation System with High-variant Occupancy Schedules

摘要

In order to reduce energy consumption while maintain high indoor air quality (LAQ, CO_2-based Demand controlled ventilation (DCV) has been investigated a lot and become trend of the future ventilation system. Among different advanced sensors, the real-time occupancy estimation (people counting) sensor has played an important role in CO_2-based demand control ventilation (DCV) systems. However, since particle diffusion indoors is slower than dynamic changes of occupant presence in transient conditions, the actual CO_2 concentration will generally lag behind actual number of occupants while dynamic infiltration may mitigate the lag effect. Therefore, simulations were conducted in this paper to provide insight into the application of occupancy estimation sensors for CO_2-based DCV and dynamic infiltration in the real world. Two different stochastic occupancy profiles for two different space types have been investigated, which were generated based on Markov-chain model and Gaussian model with high variance, respectively. It has been observed that due to the lag effect, the actual CO_2 concentration with Gaussian occupancy model is more likely to exceed the set-point value than that with Markov-chain occupancy model. Fortunately, the simulation results showed that dynamic infiltration owing to dynamic operation of building envelopes such as operable windows eliminated the lag effect. Therefore, for the space with variant occupancy profile, it is suggested to operate DCV by integrating the system with dynamic operations of building envelopes in order to improve the indoor air quality.