  Thermal comfort in indoor environments has an enormous impact on the health,
well-being, and performance of occupants. Given the focus on energy efficiency
and Internet-of-Things enabled smart buildings, machine learning (ML) is being
increasingly used for data-driven thermal comfort (TC) prediction. Generally,
ML-based solutions are proposed for air-conditioned or HVAC ventilated
buildings and the models are primarily designed for adults. On the other hand,
naturally ventilated (NV) buildings are the norm in most countries. They are
also ideal for energy conservation and long-term sustainability goals. However,
the indoor environment of NV buildings lacks thermal regulation and varies
significantly across spatial contexts. These factors make TC prediction
extremely challenging. Thus, determining the impact of the building environment
on the performance of TC models is important. Further, the generalization
capability of TC prediction models across different NV indoor spaces needs to
be studied. This work addresses these problems. Data is gathered through
month-long field experiments conducted in 5 naturally ventilated school
buildings, involving 512 primary school students. The impact of spatial
variability on student comfort is demonstrated through variation in prediction
accuracy (by as much as 71%). The influence of building environment on TC
prediction is also demonstrated through variation in feature importance.
Further, a comparative analysis of spatial variability in model performance is
done for children (our dataset) and adults (ASHRAE-II database). Finally, the
generalization capability of thermal comfort models in NV classrooms is
assessed and major challenges are highlighted.
