  Indoor thermal comfort in smart buildings has a significant impact on the
health and performance of occupants. Consequently, machine learning (ML) is
increasingly used to solve challenges related to indoor thermal comfort.
Temporal variability of thermal comfort perception is an important problem that
regulates occupant well-being and energy consumption. However, in most ML-based
thermal comfort studies, temporal aspects such as the time of day, circadian
rhythm, and outdoor temperature are not considered. This work addresses these
problems. It investigates the impact of circadian rhythm and outdoor
temperature on the prediction accuracy and classification performance of ML
models. The data is gathered through month-long field experiments carried out
in 14 classrooms of 5 schools, involving 512 primary school students. Four
thermal comfort metrics are considered as the outputs of Deep Neural Networks
and Support Vector Machine models for the dataset. The effect of temporal
variability on school children's comfort is shown through a "time of day"
analysis. Temporal variability in prediction accuracy is demonstrated (up to
80%). Furthermore, we show that outdoor temperature (varying over time)
positively impacts the prediction performance of thermal comfort models by up
to 30%. The importance of spatio-temporal context is demonstrated by
contrasting micro-level (location specific) and macro-level (6 locations across
a city) performance. The most important finding of this work is that a
definitive improvement in prediction accuracy is shown with an increase in the
time of day and sky illuminance, for multiple thermal comfort metrics.
