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

Wang, Wei; Chen, Jiayu; Hong, Tianzhen

doi:10.1016/j.buildenv.2018.04.002

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

Full Record
Other Related Research

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 »« less

Authors:

^[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

Publication Date:: 2018-04-07

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

OSTI Identifier:: 1436678

Alternate Identifier(s):: OSTI ID: 1548476

Grant/Contract Number:: AC02-05CH11231

Resource Type:: 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.

Copy to clipboard


                    Wang, Wei, Chen, Jiayu, & Hong, Tianzhen. Modeling occupancy distribution in large spaces with multi-feature classification algorithm.  United States.  https://doi.org/10.1016/j.buildenv.2018.04.002

Copy to clipboard


                    Wang, Wei, Chen, Jiayu, and Hong, Tianzhen. Sat .  
"Modeling occupancy distribution in large spaces with multi-feature classification algorithm".  United States.  https://doi.org/10.1016/j.buildenv.2018.04.002.  https://www.osti.gov/servlets/purl/1436678.

Copy to clipboard


                    
@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         = {2018},

  month        = {4}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1016/j.buildenv.2018.04.002

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 25 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Human-in-the-loop Sensing and Control for Commercial Building Energy Efficiency and Occupant Comfort

Technical Report Berges, Mario ; Munir, Sirajum ; Shelton, Charles

Most of the existing heating, ventilation and air conditioning (HVAC) systems in commercial buildings operate in a conservative manner by assuming maximum occupancy in each room during pre-specified periods of the week, leading to significant energy being wasted as rooms are over-conditioned compared to the actual requirements of the occupants. Though critical, our understanding of occupancy patterns and thermal comfort needs of the occupants in commercial buildings is lacking and it is well known that both of these quantities are stochastic and time-varying, thus requiring sensing solutions to estimate them. This project had the goal of designing, implementing and evaluatingmore »« less
https://doi.org/10.2172/1963259

Full Text Available
Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings

Journal Article Dong, Jin ; Winstead, Christopher J. ; Nutaro, James J. ; ... - Energies (Basel)

This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM)more »« less
Cited by 33
https://doi.org/10.3390/en11092427

Full Text Available
Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers

Journal Article Kim, Joyce ; Bauman, Fred ; Raftery, Paul ; ... - Building and Environment

Personal Comfort Systems (PCS) provide individual occupants local heating and cooling to meet their comfort needs without affecting others in the same space. It saves energy by relaxing ambient temperature requirements for the HVAC system. Aside from these benefits, PCS offers a wealth of data that can describe how individuals interact with heating/cooling devices in their own environment. Recently developed Internet-connected PCS chairs have unlocked this opportunity by generating continuous streams of heating and cooling usage data, along with occupancy status and environmental measurements via embedded sensors. The data allow individuals' comfort and behavior to be learned, and can informmore »« less
Cited by 53
https://doi.org/10.1016/j.buildenv.2018.11.012

Full Text Available
Understanding Occupancy Patterns in a Commercial Space

Journal Article Liu, Kin Sum ; Munir, Sirajum ; Lin, Shan ; ... - IEEE Technical Committee on Cyber-Physical Systems (TC-CPS) Newsletter

Heating, ventilation, and air conditioning (HVAC) is a prime source of energy consumption in the US. In 2006, approximately 35% of energy in the US was used for HVAC [1]. Usually building operators use a static schedule for controlling HVAC systems without having a deeper understanding of how many people use the building at different times of the day. In addition, HVAC systems operate by assuming maximum occupancy in each room, which leads to a significant energy waste, e.g., an HVAC system providing ventilation for 30 people when there are only 10 people in a room. Considering how many peoplemore »« less
Full Text Available
Nationwide Energy Saving Potential Evaluation for Office Buildings with Occupant-Based Building Controls

Journal Article Pang, Zhihong ; Chen, Yan ; Zhang, Jian ; ... - ASHRAE Transactions

The heating ventilation and air-conditioning (HVAC) system is a major contributor to building energy consumption. This paper aims to quantify the nationwide energy savings potential of implementing the occupancy-based control (OBC) for the HVAC system in typical medium-sized office buildings using a whole building simulation program (i.e., EnergyPlus). First, the medium-sized office from the DOE Commercial Prototype Building Models was modified to have detailed layout and space functions. Then, a statistic-algorithm-based tool was used to generate the dynamic occupancy schedules for various rooms. Next, both a baseline case and two OBC cases were created for the comparison based on themore »« less

Similar Records