  Thermal comfort assessment for the built environment has become more
available to analysts and researchers due to the proliferation of sensors and
subjective feedback methods. These data can be used for modeling comfort
behavior to support design and operations towards energy efficiency and
well-being. By nature, occupant subjective feedback is imbalanced as indoor
conditions are designed for comfort, and responses indicating otherwise are
less common. This situation creates a scenario for the machine learning
workflow where class balancing as a pre-processing step might be valuable for
developing predictive thermal comfort classification models with
high-performance. This paper investigates the various thermal comfort dataset
class balancing techniques from the literature and proposes a modified
conditional Generative Adversarial Network (GAN), $\texttt{comfortGAN}$, to
address this imbalance scenario. These approaches are applied to three publicly
available datasets, ranging from 30 and 67 participants to a global collection
of thermal comfort datasets, with 1,474; 2,067; and 66,397 data points,
respectively. This work finds that a classification model trained on a balanced
dataset, comprised of real and generated samples from $\texttt{comfortGAN}$,
has higher performance (increase between 4% and 17% in classification accuracy)
than other augmentation methods tested. However, when classes representing
discomfort are merged and reduced to three, better imbalanced performance is
expected, and the additional increase in performance by $\texttt{comfortGAN}$
shrinks to 1-2%. These results illustrate that class balancing for thermal
comfort modeling is beneficial using advanced techniques such as GANs, but its
value is diminished in certain scenarios. A discussion is provided to assist
potential users in determining which scenarios this process is useful and which
method works best.
