Proposal of a Regression-Based Calibration Method for eCO₂ and its Application to Occupancy Estimation

Abstract

This study addresses the challenge that the eCO₂ output of MOX-type CO₂ sensors, estimated from TVOC, is strongly affected by environmental factors such as atmospheric pressure, temperature, and humidity, making it difficult to use directly for occupancy estimation. We propose a calibration method based on regression models that explicitly incorporate atmospheric pressure as an explanatory variable. A MOX sensor and an NDIR sensor, used as a reference, were colocated in the same environment, and continuous multi-day data of CO₂, TVOC, temperature, humidity, and pressure were collected to construct the calibration models. Incorporating pressure improved accuracy, and comparison of linear regression, multiple linear regression, second-order polynomial regression, and random forest (RF) showed that RF performed best, with RMSE reduced by approximately 42% compared with the uncalibrated values. For occupancy estimation, we examined a regression model using RF with explanatory variables including CO₂, illuminance, temperature, humidity, and temporal features. Although the NDIR-only approach achieved the highest accuracy, for eCO₂, calibration improved performance, and combining it with illuminance and temporal features enabled accuracy close to that of the NDIR sensor.