Machine learning, Generative adversarial networks, Data balancing

Data Balancing for Thermal Comfort Datasets Using GANs

Data balancing is an essential preprocessing for proper training of thermal comfort estimators. The development of various machine learning methods helps to improve the performance of the thermal comfort estimation. However, thermal comfort datasets are usually imbalanced because hot/cold environments rarely appear in an air-conditioned environment. Imbalanced datasets lead to biased estimation, which is not helpful for users in environments that rarely appear. Therefore, many researchers have applied data augmentation for rare samples to balance thermal comfort datasets. Nevertheless, the imbalance in the original dataset still leads to biased data generation. In this study, we propose a data balancing method for thermal comfort datasets using conditional Wasserstein GAN with a weighted loss function.

  • Hiroki Yoshikawa, Akira Uchiyama, Teruo Higashino, "Data Balancing for Thermal Comfort Datasets Using Conditional Wasserstein GAN with a Weighted Loss Function," Proc. of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2021) Workshops, Coimbra, Portugal, November 17-18, 2021. doi:

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