Spatial features of CO2 for occupancy detection in a naturally ventilated school building
<|reference_start|>Spatial features of CO2 for occupancy detection in a naturally ventilated school building: 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.<|reference_end|>
@article{huang2024spatial,
    title={Spatial features of CO2 for occupancy detection in a naturally
  ventilated school building},
    author={Qirui Huang, Marc Syndicus, J'er^ome Frisch, Christoph van Treeck},
    journal={Indoor Environments, Volume 1, Issue 3, 2024, 100018, ISSN
  2950-3620},
    year={2024},
    doi={10.1016/j.indenv.2024.100018},
    archivePrefix={arXiv},
    eprint={2403.06643},
    primaryClass={cs.LG eess.SP}
}