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Personal thermal comfort models aim to predict an individual's thermalcomfort response, instead of the average response of a large group. Recently,machine learning algorithms have proven to be having enormous potential as acandidate for personal thermal comfort models. But, often within the normalsettings of a building, personal thermal comfort data obtained via experimentsare heavily class-imbalanced. There are a disproportionately high number ofdata samples for the "Prefer No Change" class, as compared with the "PreferWarmer" and "Prefer Cooler" classes. Machine learning algorithms trained onsuch class-imbalanced data perform sub-optimally when deployed in the realworld. To develop robust machine learning-based applications using the aboveclass-imbalanced data, as well as for privacy-preserving data sharing, wepropose to implement a state-of-the-art conditional synthetic data generator togenerate synthetic data corresponding to the low-frequency classes. Viaexperiments, we show that the synthetic data generated has a distribution thatmimics the real data distribution. The proposed method can be extended for useby other smart building datasets/use-cases.

Conditional Synthetic Data Generation for Personal Thermal Comfort Models