Predictive analytics in facilities management: A pilot study for exploring environmental comfort using wireless sensors

Summary

Purpose: Advancements in wireless sensor technology and building modelling techniques have enabled facilities managers to understand the environmental performance of the workplace in more depth than ever before. However, it is unclear to what extent this data can be used to predict subjective environmental comfort. Thus, the aim of this study was to pilot test a methodological framework for integrating real-time environmental data with subjective ratings of environmental comfort. Design/Methodology/Approach: An open-plan office was fitted with environmental sensors to measure key indoor environmental quality parameters (carbon dioxide, temperature, humidity, illumination, and sound pressure level). Additionally, building modelling techniques were used to calculate two spatial metrics (‘workspace integration’ and workspace density) for each workspace within the study area. 15 employees were repeatedly sampled across an 11-day study period, providing 78 momentary assessments of environmental comfort. Multilevel models were used to explore the extent to which the objective environmental data predicted subjective environmental comfort. Findings: Higher carbon dioxide levels were associated with more negative ratings of air quality, higher ‘workspace integration’ was associated with higher levels of distractions, and higher workspace density was associated with lower levels of social interactions. Originality/Value: To our knowledge, this is the first field study to directly explore the relationship between physical environment data collected using wireless sensors and subjective ratings of environmental comfort. The study provides proof-of-concept for a methodological framework for the integration of building analytics and human analytics.

Keywords: Environmental sensors; Smart buildings; Predictive analytics; Environmental comfort; Workplace environment; Wireless sensors
Creators:
Contributors:
Academic units: Faculty of Social Sciences and Humanities (SSH) > Academic Departments > Department of the Natural and Built Environment
Funders:
Funder NameGrant NumberFunder ID
Innovate UKUNSPECIFIED
Copyright Holders: Sheffield Hallam University, Mitie plc
Publisher of the data: SHU Research Data Archive (SHURDA)
Publication date: 2 September 2019
Data last accessed: No data downloaded yet
DOI: http://doi.org/10.17032/shu-180016
SHURDA URI: http://shurda.shu.ac.uk/id/eprint/114

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