Poster Abstract: Synthetic Personal Thermal Comfort Data Generation

Personal thermal comfort models aim to predict an individual's thermal comfort response, instead of the average response of a large group. Recently, machine learning algorithms have proven to be having enormous potential as a candidate for personal thermal comfort models. But, often within the normal settings of a building, personal thermal comfort data obtained via experiments is heavily class-imbalanced. Machine learning algorithms trained on such class-imbalanced data perform sub-optimally when deployed in the real world. To develop robust machine learning-based applications using the above class-imbalanced data, as well as for other possible downstream uses such as privacy-preserving data sharing, we propose a state-of-the-art conditional synthetic data generator to generate synthetic data for the low-frequency classes. Via experiments, we show that the synthetic data generated has a distribution that is close to the real data distribution. The proposed method can be extended for use by other smart building datasets/use cases.