| Abstract | Air infiltration has a significant impact on building energy performance and the indoor environment. An accurate estimate of air infiltration rate informs envelope retrofit decisions to improve airtightness. However, air mobility and other environmental factors, such as wind or indoor-outdoor temperature differences, often make the accurate measurement of air infiltration challenging. Further, conventional air infiltration testing approaches such as fan pressurization and tracer gas tests possess certain drawbacks limiting their applicability in commercial buildings. To address the limitations of air infiltration tests, this study proposes a low-cost inverse model-based approach for estimating air infiltration rates by extracting the naturally occurring indoor CO₂ and relative humidity (RH) data from a building automation system (BAS). These data were used to develop a linear regression model, and a tracer gas experiment was also used to verify the applicability of the proposed approach. The results indicated that the proposed method could conveniently lend itself to estimate air infiltration rates at a reasonable accuracy using existing CO₂ data; however, humidity ratio data (converted from RH and temperature sensor measurements) may only help to track building infiltration characteristics over time due to inaccuracies caused by adsorptive and desorptive properties of various building materials. |
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