Detection of occupancy profile based on carbon dioxide concentration pattern matching

Occupancy is one of major factors influencing indoor microclimate. The aim of this work was to determine the influence of this factor on indoor air quality (IAQ) on the basis of CO2 concentration measurements and statistical analysis. We wanted to identify periods of time when IAQ was strongly affected by the occupancy described by the given profile. The proposed approach consisted of several stages. The CO2 concentration was measured and recorded in the form of univariate time series. Then, the relationship between occupancy and internal structure of the time series was disclosed. There were distinguished segments based on sample periodogram. Each segment was associated with a particular occupancy profile. In order to detect how human factor represented by a given occupancy profile influences IAQ we proposed to use pattern matching. In this approach there was examined the similarity between segments of the time series and the pattern of CO2 variability, which represented a selected occupancy profile. The analysis was performed in time domain using moving time window technique. The similarity was judged based on two types of indexes, namely correlation coefficients and distance measures. It was shown that our approach may be applied to successfully detect a particular occupancy profile. The best performance was achieved when using angular distance as the similarity index. In this case we reached 82% true positive and 22% false positive detections. The proposed method may be applied in diagnostics problems to reveal sources of indoor air quality problems.