  This paper presents 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 real-time available. 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.
