Multi-task Learning for Concurrent Prediction of Thermal Comfort, Sensation, and Preference

Abstract

Zusammenfassung-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 multidimensional. Different aspects of TC are represented by various standard metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and thermal preference (TPV). The current MLbased 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 deeplearning 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 ASHRAE II database and the dataset created in this study. DeepComfort is also shown to outperform 6 popular metric-specific singletask machine learning algorithms. To the best of our knowledge, this work is the first application of Multi-task Learning to TC prediction in classrooms.

Impact of Escalating Heat Waves on Students’ Well-Being and Overall Health: A Survey of Primary School Teachers

“…In the course of our earlier field survey in India during the summer season [47,48], it was observed that numerous primary school students were sent home early after they complained about being unwell (due to overheating and the extreme heat in the classrooms). In Table 8, the percentage of students who returned home early from their school schedule because of feeling unwell due to heat, as reported by the current survey, is demonstrated.…”