Personalized Thermal Comfort Model with Decision Tree

Yuze Jiang

doi:10.4236/ica.2019.104012

Abstract

Thermal comfort is the expression of people’s satisfaction with the indoor temperature and is related to people’s working efficiency and health. In this way, it is necessary to construct a suitable environment for the user. However, even if adaptive thermal comfort has been developing rapidly for the past decades, most of the models are still developed based on simple statistical analysis such as regression models, which may not capture the complex relations between thermal comfort and the indoor thermal environment as well as differences between individual characteristics. Hence, in order to improve the accuracy of the adaptive thermal comfort model, this paper proposes a decision-tree-based thermal comfort model developed with the subset of the RP884 dataset. Then, a comfort-based HVAC controller was developed with the thermal sensation prediction results with the trained model above. As a result, the proposed controller indeed improves occupant’s thermal comfort model.

Highlights

Thermal comfort is the expression of people’s satisfaction with the indoor temperature and is related to people’s working efficiency and health
Even if adaptive thermal comfort has been developing rapidly for the past decades, most of the models are still developed based on simple statistical analysis such as regression models, which may not capture the complex relations between thermal comfort and the indoor thermal environment as well as differences between individual characteristics
In order to improve the accuracy of the adaptive thermal comfort model, this paper proposes a decision-tree-based thermal comfort model developed with the subset of the RP884 dataset

Summary

Introduction

Thermal comfort is the expression of people’s satisfaction with the indoor temperature and is related to people’s working efficiency and health. In this way, it is necessary to construct a suitable environment for the user. The usage of responsive HVAC operations with real-time occupant related information is necessary and it is able to improve building performances, including energy savings and occupant comfort level. Lu et al proposed the dynamic HVAC operations based on the number of occupants by occupant recognition with YOLO and taking both energy efficiency and thermal comfort into account and conducted an energy simulation to show the energy savings with the real-time occupant estimation system [1]. Most of studies on dynamic HVAC operations apply static thermal comfort model into the control logics, which cannot represent dynamic conditions in the real built environment

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Conclusion