Exponential pattern recognition for deriving air change rates from CO2 data
Exponential pattern recognition for deriving air change rates from CO2 data
复制标题
用于从二氧化碳数据导出空气变化率的指数模式识别
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
D. Ničković
中科院分区：
文献类型：
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作者：
Florian Wenig;P. Klanatsky;Christian Heschl;Cristinel Mateis;D. Ničković
提出了一种新的方法，用于自动确定从测量的室内CO2浓度的换气率。建议的方法建立在一个新的算法来检测指数的建立和衰减模式的CO2浓度时间序列。该概念的可行性证明了与合成数据的测试运行，显示了良好的再现先前定义的换气分布。该演示继续在两栋住宅楼的厨房和卧室测量的CO2数据集上进行测试。当使用自然或机械通风时，在两种情况下得出的换气率均在预期的分布和范围内。
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.