Using Statistical Models to Detect Occupancy in Buildings through Monitoring VOC, CO2, and Other Environmental Factors
Publication: Computing in Civil Engineering 2021
ABSTRACT
Dynamic models of occupancy patterns have shown to be effective in optimizing building-systems operations. Previous research has relied on CO2 sensors and vision-based techniques to determine occupancy patterns. Vision-based techniques provide highly accurate information; however, they are very intrusive. Therefore, motion or CO2 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 VOC level. In this paper, continuous measurements of CO2, VOC, light, temperature, and humidity were recorded in a 17,000 sq ft 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 provide 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 a proper feature selection and developing appropriate global occupancy detection models can reduce the cost and energy of data collection without a significant impact on the accuracy.
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Computing in Civil Engineering 2021
Pages: 705 - 712
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Published online: May 24, 2022
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