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This paper presents an indoor occupancy estimator with which we can estimatethe 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 LearningMachine (FS-ELM) algorithm, which is a variation of the standard ExtremeLearning Machine (ELM) but is shown to perform better for the occupancyestimation problem. The measured CO2 concentration suffers from serious spikes.We find that pre-smoothing the CO2 data can greatly improve the estimationaccuracy. In real applications, however, we cannot obtain the real-timeglobally smoothed CO2 data. We provide a way to use the locally smoothed CO2data instead, which is real-time available. We introduce a new criterion, i.e.$x$-tolerance accuracy, to assess the occupancy estimator. The proposedoccupancy estimator was tested in an office room with 24 cubicles and 11 openseats. The accuracy is up to 94 percent with a tolerance of four occupants.

Indoor occupancy estimation from carbon dioxide concentration