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)
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4MBHammad-2019.pdf - Accepted Version Available under License Spectrum Terms of Access. |
Official URL: http://dx.doi.org/10.1016/j.buildenv.2019.01.052
Abstract
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 |
| Refereed: | Yes |
| Authors: | Salimi, Shide and Liu, Zheng and Hammad, Amin |
| Journal or Publication: | Building and Environment |
| Date: | 31 January 2019 |
| Funders: |
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| 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 |
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