Exponential pattern recognition for deriving air change rates from CO2 data

A novel procedure for automated determination of air change rates from measured indoor CO 2 concentrations is proposed. The suggested approach builds upon a new algorithm to detect exponential build-up and decay patterns in CO 2 concentration time series. The feasibility of the concept is proved wit...

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Vydané v:Proceedings of the IEEE International Symposium on Industrial Electronics (Online) s. 1507 - 1512
Hlavní autori: Wenig, Florian, Klanatsky, Peter, Heschl, Christian, Mateis, Cristinel, Dejan, Nickovic
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2017
Predmet:
air change rate
Air quality
Atmospheric modeling
Buildings
concentration decay
exponential pattern recognition
Gases
indoor air quality
Mathematical model
Monitoring
tracer gas
Ventilation
ISSN:2163-5145
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Shrnutí:A novel procedure for automated determination of air change rates from measured indoor CO 2 concentrations is proposed. The suggested approach builds upon a new algorithm to detect exponential build-up and decay patterns in CO 2 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 CO 2 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.
ISSN:2163-5145
DOI:10.1109/ISIE.2017.8001469