Influential variables impacting the reliability of building occupancy sensor systems: A systematic review and expert survey

Chu, Yiyi; Mitra, Debrudra; O’Neill, Zheng; Cetin, Kristen

doi:10.1080/23744731.2021.1993672

Cited by 7 publications

(4 citation statements)

References 80 publications

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“…Also, it is hard to capture the exact coordinates through the observation, which is not as precise as the sensor-detected data. Furthermore, one persisting and inevitable major challenge in occupancy analysis is the inherently unpredictable nature of occupants [ 32 ]; therefore, this study only aims to find out the spatial pattern identified from a collection of people without specifying the unique individual cases. The study is based on a specific co-working space and the ability to generalize has not been tested.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, while the majority of studies focus on improving the accuracy of occupancy sensors for energy efficiency in the office environment [ 16 , 32 ], there is a significant potential in the analysis and prediction of occupancy in offices with the sensor-based datasets, as it could have a further implication in the post-pandemic time to inform and utilise the office design, which is discussed in the next section.…”

Section: Literature Reviewmentioning

confidence: 99%

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Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working space

Pan,

Cho,

Sun

et al. 2023

PLoS ONE

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The future of workspace is significantly shaped by the advancements in technologies, changes in work patterns and workers’ desire for an improved well-being. Co-working space is an alternative workspace solution, for cost-effectiveness, the opportunity for diverse and flexible design and multi-use. This study examined the human-centric design choices using spatial and temporal variation of occupancy levels and user behaviour in a flexible co-working space in London. Through a machine-learning-driven analysis, we investigated the time-dependent patterns, decompose space usage, calculate seat utilisation and identify spatial hotspots. The analysis incorporated a large dataset of sensor-detected occupancy data spanning 477 days, comprising more than 140 million (145×106) data points. Additionally, on-site observations of activities were recorded for 13 days spanning over a year, with 110 time instances including more than 1000 snapshots of occupants’ activities, indoor environment, working behaviour and preferences. Results showed that the shared working areas positioned near windows or in more open, connected and visible locations are significantly preferred and utilised for communication and working, and semi-enclosed space on the side with less visibility and higher privacy are preferred for focused working. The flexibility of multi-use opportunity was the most preferred feature for hybrid working. The findings offer data-driven insights for human-centric space planning and design of office spaces in the future, particularly in the context of hybrid working setups, hot-desking and co-working systems.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working space

Pan,

Cho,

Sun

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

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“…Trivedi et al [42] conducted a survey of occupancy detection systems and an overview of occupancy detection approaches as well as limitations for each system. Chu et al [43] conducted a review of occupancy sensor systems, focusing on variables that impact the system's reliability. These studies provide a comprehensive review of occupancy sensing technologies; however, without describing broader building applications and data requirements it becomes difficult to select the right technology.…”

Section: Measuring Occupancy To Support Commercial Building Operation...mentioning

confidence: 99%

“…More generally, these metrics can be expressed as a confusion matrix that quantifies true positives, true negatives, false positives, and false negatives [47], [48]. The accuracy for occupancy count can rely on standard statistical methods including mean absolute error, root mean square, and normalized root mean square error [47][49][50] [43]. Another performance metric commonly used to evaluate the performance of occupancy sensing data includes xtolerance accuracy, where tolerance x should be properly selected based on specific applications (this is a more suitable metric to assess the accuracy of occupancy count data for building-control applications such as ventilation) [46].…”

Section: Accuracymentioning

confidence: 99%

Comprehensive Simulation-based Workflow to Assess the Performance of Occupancy-based Controls and Operations in Office Buildings

Azimi¹

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Although many people have been forced to work remotely due to the pandemic, occupancy patterns in office buildings were already rapidly changing due to technological advances, enabling workers to work remotely or "telework". While having more workers telework could potentially lower an organization's need for real estate and associated costs, this style of work is not implemented in the most efficient way since many buildings still consume significant amounts of energy even if occupancy is low. Additionally, spaces are acquired assuming maximum occupancy, and cleaning and maintenance schedules do not change with respect to occupancy level, therefore wasting resources. Most services provided -from ventilation to cleaning and space -are (or should be) a function of occupancy. Yet occupancy is seldom measured in a comprehensive way such that the data can be widely used to improve building operations. There is a wide variety of applications I wish to thank members of the Building Performance Research Centre for projecting an essence of comradery and community through regular virtual seminars, discussions, and events; I am thankful to be part of such a tight-knit, diverse, and knowledgeable group.

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Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

Kula

Mitra

Chu

et al. 2023

Building and Environment

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Influential variables impacting the reliability of building occupancy sensor systems: A systematic review and expert survey

Cited by 7 publications

References 80 publications

Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working space

Future workspace needs flexibility and diversity: A machine learning-driven behavioural analysis of co-working space

Comprehensive Simulation-based Workflow to Assess the Performance of Occupancy-based Controls and Operations in Office Buildings

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

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