Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11790/820
Title: Exponential pattern recognition for deriving air change rates from CO2 data
Authors: Wenig, Florian 
Klanatsky, Peter 
Heschl, Christian 
Mateis, Cristinel 
Dejan, Nickovic 
Keywords: air change rate;tracer gas;exponential pattern recognition;indoor air quality;ventilation;concentration decay
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Source: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK, pp. 1507-1512
Project: IoSense 
Abstract: 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.
URI: http://hdl.handle.net/20.500.11790/820
Appears in Collections:Energie-Umweltmanagement

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