Demographics as Determinants of Building Occupants’ Indoor Environmental Perceptions: Insights from a Machine Learning Incremental Modeling and Analysis Approach
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 4
Abstract
The relationship between the demographical characteristics of building occupants and their perception of indoor comfort is increasingly being studied. However, the added value from accounting for such characteristics when modeling and predicting occupants’ perceptions remains unclear. An incremental machine learning (ML) modeling and analysis approach is proposed to quantify the influence of four demographical factors (gender, age, nationality, and time lived in the environment) on occupants’ perceptions of their indoor environment conditions. A three-step methodology is presented: (1) data collection through sensors and a questionnaire administered on 206 occupants of academic and office buildings in Abu Dhabi, UAE, (2) development of ML models (i.e., support vector machine, random forest, and gradient boosting) to predict occupants’ perceptions under different scenarios of demographical representation (i.e., from no representation to all demographical parameters included), and (3) analysis of the impact of demographical parameters’ inclusion on the performance of the ML models in terms of predictive accuracy, F1-scores, and computing time. Results confirm that including demographical variables could increase prediction accuracy and F1-scores by approximately 19% and 56%, respectively. However, in some instances, the inclusion of these variables reduced model performance while increasing computing time by as much as 50%. A detailed discussion is presented on the comparative performance of the different tested ML algorithms and the need to strike a balance between increasing model complexity and computational costs.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank the Abu Dhabi Department of Education and Knowledge (ADEK) for their financial support under Grant No. AARE18-063. They also wish to acknowledge the contribution of Khalifa University’s high-performance computing and research computing facilities to the results of this research.
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Journal of Computing in Civil Engineering
Volume 36 • Issue 4 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Nov 10, 2021
Accepted: Feb 14, 2022
Published online: May 2, 2022
Published in print: Jul 1, 2022
Discussion open until: Oct 2, 2022
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