IoT-based Occupancy Estimation Models for Indoor Non-Residential Environments

An IoT end to end system has been developed in this work to collect relative humidity (RH), CO₂ concentration and occupant count of a University classroom. The RH and CO₂ data has been used to compute estimates of student occupancy using regression based estimation models. Multiple linear and quantile regression models have been explored for occupancy estimation by using RH, CO₂, and both RH as well as CO₂ concentration respectively. The estimation performance of these models has been compared by using mean absolute percentage error. The quantile regression based models have been found to be the most accurate with a mean absolute percentage error of 2.47%.