  Accurate occupancy information helps to improve building energy efficiency
and occupant comfort. Occupancy detection methods based on CO2 sensors have
received attention due to their low cost and low intrusiveness. In naturally
ventilated buildings, the accuracy of CO2-based occupancy detection is
generally low in related studies due to the complex ventilation behavior and
the difficulty in measuring the actual air exchange through windows. In this
study, we present two novel features for occupancy detection based on the
spatial distribution of the CO2 concentration. After a quantitative analysis
with Support Vector Machine (SVM) as classifier, it was found that the accuracy
of occupancy state detection in naturally ventilated rooms could be improved by
up to 14.8 percentage points compared to the baseline, reaching 83.2 % (F1
score 0.84) without any ventilation information. With ventilation information,
the accuracy reached 87.6 % (F1 score 0.89). The performance of occupancy
quantity detection was significantly improved by up to 25.3 percentage points
versus baseline, reaching 56 %, with root mean square error (RMSE) of 11.44
occupants, using only CO2-related features. Additional ventilation information
further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By
incorporating spatial features, the model using only CO2-related features
revealed similar performance as the model containing additional ventilation
information, resulting in a better low-cost occupancy detection method for
naturally ventilated buildings.
