# Hybrid data driven/thermal simulation model for comfort assessment

arXiv: https://arxiv.org/abs/2309.01734 · HTML: https://mlsanity.com/paper/2309.01734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher](https://mlsanity.com/paper/2004.08780/) (2020)
- [Data generation using simulation technology to improve perception mechanism of autonomous vehicles](https://mlsanity.com/paper/2207.00191/) (2022)

## Abstract (arXiv)

Machine learning models improve the speed and quality of physical models. However, they require a large amount of data, which is often difficult and costly to acquire. Predicting thermal comfort, for example, requires a controlled environment, with participants presenting various characteristics (age, gender, ...). This paper proposes a method for hybridizing real data with simulated data for thermal comfort prediction. The simulations are performed using Modelica Language. A benchmarking study is realized to compare different machine learning methods. Obtained results look promising with an F1 score of 0.999 obtained using the random forest model.