Indoor occupancy estimation from carbon dioxide concentration

TL;DR

This paper introduces a real-time indoor occupancy estimation method using CO2 measurements, employing a novel FS-ELM algorithm and data smoothing techniques to achieve up to 94% accuracy.

Contribution

It proposes a new dynamic occupancy model and the FS-ELM algorithm, improving real-time estimation accuracy with locally smoothed CO2 data.

Findings

Achieved up to 94% accuracy in occupancy estimation

Pre-smoothing CO2 data significantly improves accuracy

The method works with real-time locally smoothed CO2 data

Abstract

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.