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Quantification of personal thermal comfort with localized airflow system based on sensitivity analysis and classification tree model

机译：基于灵敏度分析和分类树模型的局部气流系统的个人热舒适度

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摘要

Although local air movement acts as a critical factor to enhance human thermal comfort and energy efficiency, the various factors influencing such movement have led to inconsistent publications on how to evaluate and design localised airflow systems in practice. This study aims to identify the main impacting factors for a localised airflow system and predict a cooling performance based on machine learning algorithms. Three typical localised airflow forms, i.e. an isothermal air supply (IASN), non-isothermal air supply (NIASN), and floor fan (FF), were deployed. The experiments were conducted under a variety of temperature/humidity/local air velocity conditions in a well-controlled climate chamber, and a database including 1305 original samples was built. The primary results indicated that a classification tree C5.0 model showed a better prediction performance (83.99%) for a localised airflow system, with 17 input parameters in the model. Through a sensitivity analysis, 8 feature variables were quantified as having significant main effect responses on subjects' thermal sensation votes (TSV), and three environmental factors (temperature, air velocity, and relative humidity) were identified as having the most significant effects. Using the 8 sensitive factors, the C5.0 model was modified with 82.30% accuracy for subject TSV prediction. A tree model demonstrating the decision rules in the C5.0 model was obtained, with air velocity (=0 m/s, 0 m/s) as the first feature variable and root node, and temperature (= 28 degrees C, 28 degrees C) as the second feature variable and leaf node, respectively. The outcomes that provide the most influential variables and a machine learning model are beneficial for evaluating personal thermal comfort at individual levels and for guiding the application of a localised airflow system in buildings. (C) 2019 Elsevier B.V. All rights reserved.
机译：尽管当地空气运动作为提高人类热舒适度和能效的关键因素，但影响这种运动的各种因素导致了如何在实践中评估和设计局部气流系统的出版物不一致。本研究旨在确定局部气流系统的主要影响因素，并基于机器学习算法预测冷却性能。展开了三种典型的局部气流形式，即等温空气供应（IASN），非等温空气供应（NIASN）和地板风扇（FF）。实验在良好控制的气候室中的各种温度/湿度/局部空气速度条件下进行，建立了包括1305个原始样品的数据库。主要结果表明，分类树C5.0模型显示了局部气流系统的更好的预测性能（83.99％），其中模型中有17个输入参数。通过敏感性分析，量化8个特征变量被定量为对受试者热敏投票（TSV）的显着的主要效应响应，并且鉴定了三种环境因子（温度，空气速度和相对湿度）具有最显着的影响。使用8个敏感因素，C5.0模型被修改为82.30％的主体TSV预测精度。获得了展示C5.0模型中决策规则的树模型，空气速度（= 0 m / s，> 0 m / s）为第一特征变量和根节点，温度（<= 28℃， > 28℃）作为第二特征变量和叶节点。提供最有影响力的变量和机器学习模型的结果是有利于评估个体水平的个人热舒适性，并引导局部气流系统在建筑物中的应用。 （c）2019 Elsevier B.v.保留所有权利。

著录项

  • 来源
    《Energy and Buildings》 |2019年第7期|1-11|共11页
  • 作者

    Du Chenqiu; Li Baizhan; Liu Hong; Ji Yu; Yao Runming; Yu Wei;

  • 作者单位

    Chongqing Univ Minist Educ Joint Int Res Lab Green Bldg & Built Environm Chongqing 400045 Peoples R China|Chongqing Univ Minist Sci & Technol Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China;

    Chongqing Univ Minist Educ Joint Int Res Lab Green Bldg & Built Environm Chongqing 400045 Peoples R China|Chongqing Univ Minist Sci & Technol Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China;

    Chongqing Univ Minist Educ Joint Int Res Lab Green Bldg & Built Environm Chongqing 400045 Peoples R China|Chongqing Univ Minist Sci & Technol Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China;

    Chongqing Univ Minist Educ Joint Int Res Lab Green Bldg & Built Environm Chongqing 400045 Peoples R China|Chongqing Univ Minist Sci & Technol Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China;

    Chongqing Univ Minist Educ Joint Int Res Lab Green Bldg & Built Environm Chongqing 400045 Peoples R China|Univ Reading Sch Built Environm Reading RG6 6AW Berks England;

    Chongqing Univ Minist Educ Joint Int Res Lab Green Bldg & Built Environm Chongqing 400045 Peoples R China|Chongqing Univ Minist Sci & Technol Natl Ctr Int Res Low Carbon & Green Bldg Chongqing 400045 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Localised airflow system; Influencing factors; Sensitivity analysis; Classification tree model; Thermal sensation prediction;

    机译：局部气流系统;影响因素;敏感性分析;分类树模型;热敏检测;

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