Hybrid data driven/thermal simulation model for comfort assessment

• URL: http://arxiv.org/abs/2309.01734v1
• Date: Mon, 4 Sep 2023 17:39:07 GMT
• Title: Hybrid data driven/thermal simulation model for comfort assessment
• Authors: Romain Barbedienne, Sara Yasmine Ouerk, Mouadh Yagoubi, Hassan Bouia, Aurelie Kaemmerlen, Benoit Charrier
• Abstract summary: This paper proposes a method for hybridizing real data with simulated data for thermal comfort prediction. A benchmarking study is realized to compare different machine learning methods.
• Score: 0.3495246564946556
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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