Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.buildenv.2021.108532
Title: Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec
Authors: Abdelrahman, Mahmoud M 
Chong, Adrian 
Miller, Clayton 
Keywords: Science & Technology
Technology
Construction & Building Technology
Engineering, Environmental
Engineering, Civil
Engineering
Spatial-temporal modeling
Building information models
Graph network structure
Personal thermal comfort model
Digital twin
INDOOR ENVIRONMENTAL-QUALITY
IEQ
Issue Date: 1-Jan-2022
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Citation: Abdelrahman, Mahmoud M, Chong, Adrian, Miller, Clayton (2022-01-01). Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec. BUILDING AND ENVIRONMENT 207 : 10.1016/j.buildenv.2021.108532. ScholarBank@NUS Repository. https://doi.org/10.1016/j.buildenv.2021.108532
Abstract: Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person's thermal preference. The spatial context of a building can provide information to models about the windows, walls, heating and cooling sources, air diffusers, and other factors that create micro-environments that influence thermal comfort. Due to spatial heterogeneity, it is impractical to position sensors at a high enough resolution to capture all conditions. This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting spatial–temporal occupants’ indoor environmental preferences. Build2Vec utilizes the spatial data from the Building Information Model (BIM) and indoor localization in a real-world setting. This framework uses longitudinal intensive thermal comfort subjective feedback from smart watch-based ecological momentary assessments (EMA). The aggregation of these data is combined into a graph network structure (i.e., objects and relations) and used as input for a classification model to predict occupant thermal preference. The results of a test implementation show 14%–28% accuracy improvement over a set of baselines that use conventional thermal preference prediction input variables.
Source Title: BUILDING AND ENVIRONMENT
URI: https://scholarbank.nus.edu.sg/handle/10635/229411
ISSN: 03601323
1873684X
DOI: 10.1016/j.buildenv.2021.108532
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