Hybrid data driven/thermal simulation model for comfort assessment
Romain Barbedienne Sara Yasmine Ouerk Mouadh Yagoubi Hassan Bouia Aurelie Kaemmerlen Benoit Charrier
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.