Occupancy estimation with environmental sensors: The possibilities and limitations

Occupancy detection and estimation in buildings paves the way to improve the utilization of lighting and HVAC systems, induce energy savings and enhance the well-being of the occupants. This paper presents a comparative study of state-of-art machine learning techniques that solve two different occupancy monitoring problems using environmental sensor data. One is the regression problem that estimates the actual count of occupants while the other is the classification problem which estimates the level of occupancy (empty, sparse, full). The results of the best performing machine learning techniques that solve both problems for the open dataset from the University of Southern Denmark, Odense are presented to compare the accuracy of both approaches and the ease of implementation. The impact of $C{O}_2$, temperature, and humidity features on the occupancy count/levels and detection accuracy (occupied versus unoccupied) are studied. Comprehensive analysis with different combinations of environmental features and other free features such as time-of-day along with different sampling techniques for training and testing are performed to understand how such models can be adapted for actual deployment. Our results indicate detection accuracy between 66% to 82% for different sampling schemes; with day-based sampling showing a better performance while random sampling generically showcasing lower accuracy (66.2%). The occupancy estimation (levels or counts) has accuracy in the range of 69% to 79% for random sampling and 71% to 80% for day-based sampling. Finally, results demonstrate that models based single environmental sensor data streams do not perform as well as the models with sensor fusion.