Perccs: Person-Count From Carbon Dioxide Using Sparse Non-Negative Matrix Factorization

Occupancy count in rooms is valuable for applications such as room utilization, opportunistic meeting support, and efficient heating-cooling operations. Few buildings, however, have the means of knowing occupancy beyond simple binary presence-absence. In this paper we present the PerCCS algorithm that explores the possibility of estimating person count from CO2 sensors already integrated in everyday room airconditioning infrastructure. PerCSS uses task-driven Sparse Non-negative Matrix Factorization (SNMF) to learn a nonnegative low-dimensional representation of the CO2 data in the preprocessing stage. This denoised CO2 acts as the predictor variable for estimating occupancy count using Ensemble Least Square Regression. We tested the algorithm to estimate 15 minutes average occupancy count from a classroom of capacity 42 and compared its performance against existing methods from the literature. PerCSS estimates occupancy with a normalized mean squared error (NMSE) of 0.075 and outperformed our comparative methods in predicting occupancy count with 91 % and 15 % for exact occupancy estimation, when the room was unoccupied and occupied respectively, whereas the competing methods failed mostly.