Login | Register

Occupancy prediction model for open-plan offices using real-time location system and inhomogeneous Markov chain


Occupancy prediction model for open-plan offices using real-time location system and inhomogeneous Markov chain

Salimi, Shide, Liu, Zheng and Hammad, Amin ORCID: https://orcid.org/0000-0002-2507-4976 (2019) Occupancy prediction model for open-plan offices using real-time location system and inhomogeneous Markov chain. Building and Environment . ISSN 03601323 (In Press)

[thumbnail of Hammad-2019.pdf]
Text (application/pdf)
Hammad-2019.pdf - Accepted Version
Available under License Spectrum Terms of Access.

Official URL: http://dx.doi.org/10.1016/j.buildenv.2019.01.052


Implementing intelligent control strategies of building systems can significantly improve building energy performance and maintain or increase occupants' comfort level. However, these control strategies are dependent on the occupancy models. A good occupancy prediction model requires enough input data pertinent to the occupants' space utilization patterns. However, most of the occupancy detection systems cannot provide this detailed information. As a result, most of the research works that considered shared multi-occupied offices did not distinguish between different individuals. Therefore, their practicality is reduced when they are used for open-plan offices. In this study, the occupancy modeling (i.e., occupants’ profiles) has been further enhanced using inhomogeneous Markov chain prediction model based on real occupancy data collected by a Real Time Locating System (RTLS). After extracting the detailed occupancy information with varying time-steps from the collected RTLS occupancy data, an adaptive probabilistic occupancy prediction model is developed. The comparison between the occupancy profiles resulting from the prediction model and the actual profiles showed that the prediction model was able to capture the actual behavior of occupants at occupant and zone levels with high accuracy. The proposed model distinguishes the temporal behavior of different occupants within an open-plan office and can be used for various levels of resolution required for the application of intelligent, occupancy-centered local control strategies of different building systems. This would eventually lead to a more robust control of building systems as well as more satisfied occupants.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Authors:Salimi, Shide and Liu, Zheng and Hammad, Amin
Journal or Publication:Building and Environment
Date:31 January 2019
  • Alexander Graham Bell Canada Graduate Scholarship-Doctoral
  • Natural Sciences and Engineering Research Council of Canada
Digital Object Identifier (DOI):10.1016/j.buildenv.2019.01.052
Keywords:Adaptive occupancy prediction modelling; Real-time occupancy monitoring; Inhomogeneous Markov chain; Open-plan offices; Intelligent energy management; Adaptive local control strategies
ID Code:984991
Deposited By: Michael Biron
Deposited On:12 Feb 2019 23:12
Last Modified:31 Jan 2021 02:00


SUSRIS International energy outlook 25 July 2013. [Online]. Available: http://susris.com/2013/07/25/international-energy-outlook-2013/ (2013)

Annex 53 Total Energy Use in Buildings: Analysis and Evaluation Methods AECOM Ltd, United Kingdom (2016)

C. Wang, D. Yan, Y. Jiang A novel approach for building occupancy simulation Building Simulation, 4 (2) (2011), pp. 149-167

X. Feng, D. Yan, T. Hong Simulation of occupancy in buildings Energy Build., 87 (2015), pp. 348-359

D. Yan, W. O'Brien, T. Hong, X. Feng, H.B. Gunay, F. Tahmasebi, A. Mahdavi Occupant behavior modeling for building performance simulation: current state and future challenges Energy Build., 107 (2015), pp. 264-278

W. Shen, G. Newsham, B. Gunay Leveraging existing occupancy-related data for optimal control of commercial office buildings: a review Adv. Eng. Inf., 33 (2017), pp. 230-242

T. Hong, S. D'Oca, W.J. Turner, S.C. Taylor-Lange An ontology to represent energy-related occupant behavior in buildings. Part I: introduction to the DNAs framework Build. Environ., 92 (2015), pp. 764-777

N. Li, G. Calis, B. Becerik-Gerber Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations Autom. ConStruct., 24 (2012), pp. 89-99

S. Salimi, A. Hammad Critical Review and Research Roadmap of Office Building Energy Management Based on Occupancy Monitoring Energy and Buildings (2018)

M.M. Soltani, A. Motamedi, A. Hammad Enhancing Cluster-based RFID Tag Localization using artificial neural networks and virtual reference tags Autom. ConStruct., 54 (2015), pp. 93-105

J. Yang, M. Santamouris, S.E. Lee Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings Energy Build., 121 (2016), pp. 344-349

W. Wang, J. Chen, X. Song Modeling and predicting occupancy profile in office space with a Wi-Fi probe-based Dynamic Markov Time-Window Inference approach Build. Environ., 124 (2017), pp. 130-142

Quuppa One for all [Online]. Available: http://quuppa.com/applications/ (2017)

W.K. Chang, T. Hong Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data In Building Simulation, 6 (1) (2013), pp. 23-32

J. Virote, R. Neves-Silva Stochastic models for building energy prediction based on occupant behavior assessment Energy Build., 53 (2012), pp. 183-193

S. Wei, J. Yong, B. Ng, J. Tindall, Q. Lu, H. Du Occupant adaptive behaviour: an effective method towards energy efficient buildings CIBSE Technical Symposium, London, UK (2018)

Y. Yamaguchi, Y. Shimoda, M. Mizuno Development of district energy system simulation model based on detailed energy demand model In Proceeding of Eighth International IBPSA Conference, Eindhoven, Netherlands (2003)

J. Page, D. Robinson, N. Morel, J.L. Scartezzini A generalised stochastic model for the simulation of occupant presence Energy Build., 40 (2) (2008), pp. 83-98

K.P. Lam, M. Höynck, B. Dong, B. Andrews, Y.S. Chiou, R. Zhang, D. Benitez, J. Choi Occupancy detection through an extensive environmental sensor network in an open-plan office building IBPSA Building Simulation, Glasgow, Scotland (2009), p. 145

B. Dong, B. Andrews, K.P. Lam, M. Höynck, R. Zhang, Y.S. Chiou, D. Benitez An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network Energy Build., 42 (7) (2010), pp. 1038-1046

B. Dong, K.P. Lam Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network Journal of Building Performance Simulation, 4 (4) (2011), pp. 359-369

V.L. Erickson, M.Á. Carreira-Perpiñán, A.E. Cerpa OBSERVE: occupancy-based system for efficient reduction of HVAC energy Information Processing in Sensor Networks (IPSN), 10th International Conference, Chicago, Illinois(2011)

Z. Han, R. X. Gao and Z. Fan, "Occupancy and indoor environment quality sensing for smart buildings," in In Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, 2012.

M. Milenkovic, O. Amft Recognizing energy-related activities using sensors commonly installed in office buildings Procedia Computer Science, 19 (2013), pp. 669-677

J.R. Dobbs, B.M. Hencey Predictive HVAC control using a Markov occupancy model American Control Conference (ACC), Portland, Oregon, USA (2014)

J.R. Dobbs, B.M. Hencey Model predictive HVAC control with online occupancy model Energy Build., 82 (2014), pp. 675-684

B. Ai, Z. Fan, R.X. Gao Occupancy estimation for smart buildings by an auto-regressive hidden Markov model American Control Conference (ACC), Portland, Oregon, USA (2014)

Z. Yang, B. Becerik-Gerber Modeling personalized occupancy profiles for representing long term patterns by using ambient context Build. Environ., 78 (2014), pp. 23-35

Z. Chen, J. Xu, Y.C. Soh Modeling regular occupancy in commercial buildings using stochastic models Energy Build., 103 (2015), pp. 216-223

Z. Chen, Y.C. Soh Modeling building occupancy using a novel inhomogeneous Markov chain approach Automation Science and Engineering (CASE), 2014 IEEE International Conference (2014)

C. Sandels, J. Widén, L. Nordström Simulating occupancy in office buildings with non-homogeneous Markov chains for demand response analysis Power & Energy Society General Meeting (2015)

Z. Wang, Y. Ding An occupant-based energy consumption prediction model for office equipment Energy Build., 109 (2015), pp. 12-22

S. Jain, N. Madamopoulos Ahorrar: indoor occupancy counting to enable smart energy efficient office buildings Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom) (2016)

K. Curran, E. Furey, T. Lunney, J. Santos, D. Woods, A. McCaughey An evaluation of indoor location determination technologies J. Locat. Based Serv., 5 (2) (2011), pp. 61-78

A. Akanmu, S.H. Rasheed, I.A. Qader Spatial mapping approach to component tracking using RTLS system Architectural Engineering Conference 2013 (2013)

S. Azzouzi, M. Cremer, U. Dettmar, T. Knie, R. Kronberger Improved AoA based localization of UHF RFID tags using spatial diversity RFID-technologies and Applications (RFID-TA) (2011)

Quuppa Unique Technology," 01 12 2017 [Online]. Available: http://quuppa.com/technology/

R. Serfozo Basics of Applied Stochastic Processes, Probability and its Applications, Verlag Berlin Heidelberg Springer (2009)

R. Douc, E. Moulines, J.S. Rosenthal Quantitative bounds on convergence of time-inhomogeneous Markov chains Ann. Appl. Probab., 14 (4) (2004), pp. 1643-1665

I. Richardson, M. Thomson, D. Infield A high-resolution domestic building occupancy model for energy demand simulations Energy Build., 40 (8) (2008), pp. 1560-1566

Z. Liu Simulation of local climate control in shared offices based on occupants locations and preferences A Thesis in the Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Canada (2017)

Grizzly Analytics Seeing Quuppa's indoor location technology at MWC 2015 [Online]. Available: http://grizzlyanalytics.blogspot.ca/2015/03/seeing-quuppas-indoor-location.html (2015), Accessed 15th Apr 2017

Quuppa Intelligent Locating System™, Unique Technology (2016) [Online]. Available: http://quuppa.com/technology/

Q. I. L. System™LD-7L long range HAIP locator," 23 September 2014
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top