  Collecting intensive longitudinal thermal preference data from building
occupants is emerging as an innovative means of characterizing the performance
of buildings and the people who use them. These techniques have occupants
giving subjective feedback using smartphones or smartwatches frequently over
the course of days or weeks. The intention is that the data will be collected
with high spatial and temporal diversity to best characterize a building and
the occupant's preferences. But in reality, leaving the occupant to respond in
an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant
data. This paper outlines a scenario-based (virtual experiment) method for
optimizing data sampling using a smartwatch to achieve comparable accuracy in a
personal thermal preference model with fewer data. This method uses
BIM-extracted spatial data and Graph Neural Network-based (GNN) modeling to
find regions of similar comfort preference to identify the best scenarios for
triggering the occupant to give feedback. This method is compared to two
baseline scenarios that use conventional zoning and a generic 4x4 square meter
grid method from two field-based data sets. The results show that the proposed
Build2Vec method has an 18-23\% higher overall sampling quality than the
spaces-based and square-grid-based sampling methods. The Build2Vec method also
performs similar to the baselines when removing redundant occupant feedback
points but with better scalability potential.
