  Dynamic models of occupancy patterns have shown to be effective in optimizing
building-systems operations. Previous research has relied on CO$_2$ sensors and
vision-based techniques to determine occupancy patterns. Vision-based
techniques provide highly accurate information; however, they are very
intrusive. Therefore, motion or CO$_2$ sensors are more widely adopted
worldwide. Volatile Organic Compounds (VOCs) are another pollutant originating
from the occupants. However, a limited number of studies have evaluated the
impact of occupants on the VOC level. In this paper, continuous measurements of
CO$_2$, VOC, light, temperature, and humidity were recorded in a 17,000 sqft
open office space for around four months. Using different statistical models
(e.g., SVM, K-Nearest Neighbors, and Random Forest) we evaluated which
combination of environmental factors provides more accurate insights on
occupant presence. Our preliminary results indicate that VOC is a good
indicator of occupancy detection in some cases. It is also concluded that
proper feature selection and developing appropriate global occupancy detection
models can reduce the cost and energy of data collection without a significant
impact on accuracy.
