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A Random Forest Classification Algorithm Based Personal Thermal Sensation Model for Personalized Conditioning System in Office Buildings
The Computer Journal ( IF 1.4 ) Pub Date : 2021-01-27 , DOI: 10.1093/comjnl/bxaa165
Qing Yun Li 1 , Jie Han 2 , Lin Lu 1
Affiliation  

The personal thermal sensation model is used as the main component for personalized conditioning system, which is an effective method to fulfill thermal comfort requirements of the occupants, considering the energy consumption. The Random Forest classification algorithm based thermal sensation model is developed in this study, which combines indoor air quality parameters, personal information, physiological factors and occupancy preferences on selection of 7-level of sensation: cold, cool, slightly cool, neutral, slightly warm, warm and hot. Our model shows better functionality, as well as performance and factor selection. As a result, our method has achieved 70.2% accuracy, comparing with the 57.4% accuracy of support vector machine, and 67.7% accuracy of neutral network in an ASHRAE RP-884 database. Therefore, our newly developed model can be used in personalized thermal adjustment systems with intelligent control functions.

中文翻译:

基于随机森林分类算法的办公大楼个性化空调系统的个人热感模型

个人的热感觉模型用作个性化调节系统的主要组成部分,考虑到能耗,这是一种满足居住者对热舒适性要求的有效方法。本研究开发了基于随机森林分类算法的热感模型,该模型结合室内空气质量参数,个人信息,生理因素以及在7种感官选择上的居住偏好:冷,凉,微凉,中性,微热,又热又热。我们的模型显示出更好的功能,以及性能和因素选择。结果,与ASHRAE RP-884数据库中的支持向量机的57.4%的准确性和中性网络的67.7%的准确性相比,我们的方法已达到70.2%的准确性。所以,
更新日期:2021-01-27
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