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Demand control ventilation (DCV) is one of the most energy efficient ways to achieve the optimum indoor air quality. It is capable of delivering the amount of outdoor air into the space as needed. ASHRAE 62.1 standard recommends the ventilation rate required for a space to protect public health. Thus, the study of DCV has to comply with ASHRAE standard or other similar standards.
Considering that the standard is updated every few years, the DCV methods studied under the previous standards also need review. In ASHRAE Standard 62.1-2004, the ventilation rate was updated with a building area related component, which makes the DCV more challenging to implement.
Although many studies have been conducted, gaps are still discovered since most of the studies are based on the similar assumptions about the Terminal Box (TBX) minimum airflow setting and normally ignore that the minimum airflow setting greatly influences the zone ventilation rate and system energy consumption.
In this dissertation, an innovative IDCV method is proposed, which studied the annual system energy consumption and indoor air quality in terms of zone CO2 through simulation. Field experiment was conducted as well to validate the proposed method. The result of the proposed method is compared to the other two control methods. One is the widely applied method of setting the outdoor air damper at a fixed position, while the other one is the MZE method introduced in the ASHRAE standard. The simulation results showed that with the proposed method, the system energy consumption was significantly reduced. For cities from 16 climate zones in the US, about 2% to 27% savings was created when compared to the method of using 20% of TBX minimum setting, and 3% to 40% of annual savings was achieved when compared to the method of using 30% of TBX minimum setting. The experiment results showed that the proposed method reduced the airflow of TBXs by 20% and the supply fan speed by 11%. Results from both simulation and experiment demonstrated that the zone CO2 level was well controlled by the proposed method.
Advisers: Josephine Lau and Mingsheng Liu