Modeling occupancy distribution in large spaces with multi-feature classification algorithm
[1];
[2];
[3]
- City University of Hong Kong (Hong Kong). Department of Architecture and Civil Engineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division
- City University of Hong Kong (Hong Kong). Department of Architecture and Civil Engineering
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division
We present that occupancy information enables robust and flexible control of heating, ventilation, and air-conditioning (HVAC) systems in buildings. In large spaces, multiple HVAC terminals are typically installed to provide cooperative services for different thermal zones, and the occupancy information determines the cooperation among terminals. However, a person count at room-level does not adequately optimize HVAC system operation due to the movement of occupants within the room that creates uneven load distribution. Without accurate knowledge of the occupants’ spatial distribution, the uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolution occupancy distribution is often perceived as a bottleneck for future improvements to HVAC operation efficiency. To fill this gap, this study proposes a multi-feature k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable, low-cost Bluetooth Low Energy (BLE) networks. An on-site experiment was conducted in a typical office of an institutional building to demonstrate the proposed methods, and the experiment outcomes of three case studies were examined to validate detection accuracy. One method based on City Block Distance (CBD) was used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. Finally, the results show the accuracy when CBD = 1 is over 71.4% and the accuracy when CBD = 2 can reach up to 92.9%.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1436678
- Alternate ID(s):
- OSTI ID: 1548476
- Journal Information:
- Building and Environment, Vol. 137, Issue C; ISSN 0360-1323
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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