Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial–temporal proximity data from Build2Vec

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 p...

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Vydáno v:Building and environment Ročník 207; s. 108532
Hlavní autoři: Abdelrahman, Mahmoud M., Chong, Adrian, Miller, Clayton
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.01.2022
Elsevier BV
Témata:
Building information modeling
Building information models
Building management systems
Diffusers
Digital twin
Digital twins
Graph network structure
Heterogeneity
Indoor environments
Learning algorithms
Localization
Machine learning
Personal thermal comfort model
Position sensing
Predictions
Preferences
Spatial data
Spatial heterogeneity
Spatial–temporal modeling
Spatiotemporal data
Thermal comfort
Spatial–temporal modeling
Digital twin
Building information models
Graph network structure
Personal thermal comfort model
ISSN:0360-1323, 1873-684X
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Shrnutí: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. •Occupants’ location in a building can impact their thermal comfort preferences.•Spatial proximity of occupants with BIM model objects can enhance prediction accuracy.•Similar zones of preference can be extracted using a graph network structure.•Personal thermal comfort models using spatial data can outperform baselines by 14%–28%
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ISSN:0360-1323
1873-684X
DOI:10.1016/j.buildenv.2021.108532