Using Statistical Models to Detect Occupancy in Buildings through Monitoring VOC, CO$_2$, and other Environmental Factors
This work addresses occupancy detection for building energy optimization, but it is incremental as it builds on existing methods by adding VOC data and feature selection.
The paper tackled the problem of detecting occupancy in buildings by evaluating environmental factors like VOC, CO2, light, temperature, and humidity using statistical models such as SVM, K-Nearest Neighbors, and Random Forest, finding that VOC can be a good indicator in some cases and that proper feature selection can reduce costs and energy without significantly impacting accuracy.
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.