Indoor occupancy estimation from carbon dioxide concentration

This paper developed an indoor occupancy estimator with which we can estimate the number of real-time indoor occupants based on the carbon dioxide (CO₂) measurement. The estimator is actually a dynamic model of the occupancy level. To identify the dynamic model, we propose the Feature Scaled Extreme Learning Machine (FS-ELM) algorithm, which is a variation of the standard Extreme Learning Machine (ELM) but is shown to perform better for the occupancy estimation problem. The measured CO₂ concentration suffers from serious spikes. We find that pre-smoothing the CO₂ data can greatly improve the estimation accuracy. In real applications, however, we cannot obtain the real-time globally smoothed CO₂ data. We provide a way to use the locally smoothed CO₂ data instead, which is available in real-time. We introduce a new criterion, i.e. x-tolerance accuracy, to assess the occupancy estimator. The proposed occupancy estimator was tested in an office room with 24 cubicles and 11 open seats. The accuracy is up to 94%percent with a tolerance of four occupants.