A Preliminary Study on Calibration Method of Low-Cost MOX-type CO2 Sensors for Indoor Occupancy Estimation

Abstract

This study tackles the challenge that the eCO2 output of low-cost MOX-based CO2 sensors, estimated from TVOC measurements, is highly sensitive to environmental factors―particularly barometric pressure, along with temperature and humidity―making it unreliable for direct use in indoor occupancy estimation. To address this issue, we propose a regression-based calibration approach that explicitly incorporates barometric pressure as a key input. By co-locating a MOX-type sensor with a high-precision NDIR reference instrument in the same environment and continuously collecting multi-day data on CO2, TVOC, temperature, humidity, and pressure, we train a calibration model. The calibrated eCO2 values, combined with temporal and environmental features, are then used as inputs to an indoor occupancy estimation model. We then input the calibrated eCO2 values together with temporal and environmental features into an indoor occupancy estimation model and demonstrate its effectiveness by comparing performance against a baseline that uses the raw eCO2 output.