Occupancy Detection via Environmental Sensing

Abstract

Sensing by proxy (SbP) is proposed in this paper as a sensing paradigm for occupancy detection, where the inference is based on “proxy” measurements such as temperature and CO2 concentrations. The effects of occupants on indoor environments are captured by constitutive models comprising a coupled partial differential equation-ordinary differential equation system that exploits the spatial and physical features. Sensor fusion of multiple environmental parameters is enabled in the proposed framework. We report on experiments conducted under simulated conditions and real-life circumstances, when the variation of occupancy follows a schedule as the ground truth. The inference of the number of occupants in the room based on CO2 concentration at the air return and air supply vents by our approach achieves an overall mean squared error of 0.6044 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Results from the projected ventilation analysis show that SbP can potentially save 55% of total ventilation compared with the traditional fixed schedule ventilation strategy, while at the same time maintain a reasonably comfort profile for the occupants.