Are You Comfortable Now: Deep Learning the Temporal Variation in Thermal Comfort in Winters

TL;DR

This study enhances thermal comfort prediction models in smart buildings by incorporating temporal factors like circadian rhythm and outdoor temperature, demonstrating significant improvements in accuracy through extensive field data analysis.

Contribution

It introduces a novel approach that considers temporal variability and outdoor temperature effects, improving ML-based thermal comfort predictions in real-world settings.

Findings

Prediction accuracy varies up to 80% with time of day.

Outdoor temperature improves model performance by up to 30%.

Temporal factors like sky illuminance significantly enhance predictions.

Abstract

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.