Exponential pattern recognition for deriving air change rates from CO2 data
Institute of Electrical and Electronics Engineers (IEEE)
2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK, pp. 1507-1512
A novel procedure for automated determination of air change rates from measured indoor CO2 concentrations is proposed. The suggested approach builds upon a new algorithm to detect exponential build-up and decay patterns in CO2 concentration time series. The feasibility of the concept is proved with a test run on synthetic data that shows a good reproduction of the previously defined air change distribution. The demonstration continues with test runs on CO2 datasets measured in the kitchen and the sleeping room of two residential buildings. The derived air change rates were within the expected distributions and ranges in both cases when natural or mechanical ventilation was used.
air change rate
exponential pattern recognition
indoor air quality