  Indoor thermal comfort immensely impacts the health and performance of
occupants. Therefore, researchers and engineers have proposed numerous
computational models to estimate thermal comfort (TC). Given the impetus toward
energy efficiency, the current focus is on data-driven TC prediction solutions
that leverage state-of-the-art machine learning (ML) algorithms. However, an
indoor occupant's perception of indoor thermal comfort (TC) is subjective and
multi-dimensional. Different aspects of TC are represented by various standard
metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and
thermal preference (TPV). The current ML-based TC prediction solutions adopt
the Single-task Learning approach, i.e., one prediction model per metric.
Consequently, solutions often focus on only one TC metric. Moreover, when
several metrics are considered, multiple TC models for a single indoor space
lead to conflicting predictions, making real-world deployment infeasible. This
work addresses these problems. With the vision toward energy conservation and
real-world application, naturally ventilated primary school classrooms are
considered. First, month-long field experiments are conducted in 5 schools and
14 classrooms, including 512 unique student participants. Further,
"DeepComfort," a Multi-task Learning inspired deep-learning model is proposed.
DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV,
simultaneously, through a single model. It demonstrates high F1-scores,
Accuracy (>90%), and generalization capability when validated on the ASHRAE-II
database and the dataset created in this study. DeepComfort is also shown to
outperform 6 popular metric-specific single-task machine learning algorithms.
To the best of our knowledge, this work is the first application of Multi-task
Learning to thermal comfort prediction in classrooms.
