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Title: Modeling occupancy distribution in large spaces with multi-feature classification algorithm

Abstract

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.more » 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%.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]
  1. 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
  2. City University of Hong Kong (Hong Kong). Department of Architecture and Civil Engineering
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1436678
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Building and Environment
Additional Journal Information:
Journal Volume: 137; Journal Issue: C; Journal ID: ISSN 0360-1323
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; HVAC loads; Multi-feature classification algorithm; Occupancy distribution; Energy efficiency; Occupancy-based control

Citation Formats

Wang, Wei, Chen, Jiayu, and Hong, Tianzhen. Modeling occupancy distribution in large spaces with multi-feature classification algorithm. United States: N. p., 2018. Web. doi:10.1016/j.buildenv.2018.04.002.
Wang, Wei, Chen, Jiayu, & Hong, Tianzhen. Modeling occupancy distribution in large spaces with multi-feature classification algorithm. United States. doi:10.1016/j.buildenv.2018.04.002.
Wang, Wei, Chen, Jiayu, and Hong, Tianzhen. Sat . "Modeling occupancy distribution in large spaces with multi-feature classification algorithm". United States. doi:10.1016/j.buildenv.2018.04.002.
@article{osti_1436678,
title = {Modeling occupancy distribution in large spaces with multi-feature classification algorithm},
author = {Wang, Wei and Chen, Jiayu and Hong, Tianzhen},
abstractNote = {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%.},
doi = {10.1016/j.buildenv.2018.04.002},
journal = {Building and Environment},
number = C,
volume = 137,
place = {United States},
year = {Sat Apr 07 00:00:00 EDT 2018},
month = {Sat Apr 07 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
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