  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.
