# Conditional Synthetic Data Generation for Personal Thermal Comfort Models

arXiv: https://arxiv.org/abs/2203.05242 · HTML: https://mlsanity.com/paper/2203.05242/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)
- [Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization](https://mlsanity.com/paper/2105.08881/) (2021)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection](https://mlsanity.com/paper/1901.04619/) (2019)
- [A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks](https://mlsanity.com/paper/1910.02217/) (2019)
- [Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure](https://mlsanity.com/paper/1910.07899/) (2019)
- [Balancing thermal comfort datasets: We GAN, but should we?](https://mlsanity.com/paper/2009.13154/) (2020)
- [CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training](https://mlsanity.com/paper/2108.11368/) (2021)

## Built on by (most cited first, 1 of 1)

- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)

## Abstract (arXiv)

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 are heavily class-imbalanced. There are a disproportionately high number of data samples for the "Prefer No Change" class, as compared with the "Prefer Warmer" and "Prefer Cooler" classes. 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 privacy-preserving data sharing, we propose to implement a state-of-the-art conditional synthetic data generator to generate synthetic data corresponding to the low-frequency classes. Via experiments, we show that the synthetic data generated has a distribution that mimics the real data distribution. The proposed method can be extended for use by other smart building datasets/use-cases.