Estimation of the real-time infiltration rate using a low carbon dioxide concentration. (October 2021) - Park, Sowoo; Choi, Paul; Song, Doosam; Koo, Junemo
Abstract: The real-time infiltration rate in a building is an important factor in controlling ventilation for energy saving and achieving acceptable indoor air quality. Recently, the occupant-generated CO2 tracer gas method has received considerable attention in that there is no need to inject a tracking gas and it can be easily calculated real-time infiltration rate through indoor and outdoor CO2 monitoring. However, data noise at low CO2 concentrations and a small drop in indoor concentrations during unoccupied periods can increase uncertainty in predicting infiltration rates. Besides, using an arbitrary reference value as the background CO2 concentration can lead to over or underestimation of the predicted values. The purpose of this study is to present an alternative form of the mass-balance equation for determining real-time infiltration rates and to review the issues that need to be considered in actual calculations. Field measurements were performed in a test room where four people work intermittently. In this study, an appropriate time interval setting method that exceeds the measurement uncertainty limit of the device is proposed, and a moving average technique is used to suppress noise signals in a low CO2 concentration. In addition, it is recommended to use the hourly average value of outdoor concentration and data with an indoor CO2 concentration higher than 1000 ppm. The originality of this work is to quantitatively analyze the uncertainty effect of outdoor carbon dioxide concentration information and to provide clear guidelines for determining the valid data interval carbon dioxide concentrations in predicting infiltration rates. Highlights: Real-time infiltration rate was calculated using low carbon dioxide concentration. A new form of the mass-balance equation was derived to estimate infiltration rates. Issues of real-time infiltration prediction using low concentration were discussed. A method to suppress noise signal and improve the linearity of data was suggested. A method of determining time range to reduce measurement uncertainty was presented.