Prediction of indoor thermal comfort level of high-speed railway station based on deep forest

doi:10.11772/j.issn.1001-9081.2020060888

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 258-264.DOI: 10.11772/j.issn.1001-9081.2020060888

Special Issue: 第八届中国数据挖掘会议(CCDM 2020)

• China Conference on Data Mining 2020 (CCDM 2020) • Previous Articles Next Articles

Prediction of indoor thermal comfort level of high-speed railway station based on deep forest

CHEN Yanru¹, ZHANG Tujingwa², DU Qian¹, RAN Maoliang¹, WANG Hongjun³

1. School of Economics and Management, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
2. Building Engineering Design and Research Institute, China Railway Eryuan Engineering Group Company Limited, Chengdu Sichuan 610031, China;
3. School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 611756, China

Received:2020-05-31 Revised:2020-07-24 Online:2021-01-10 Published:2020-09-02
Supported by:
This work is partially supported by the National Key Research and Development Program of China (2018YFC0705000).

基于深度森林的高铁站室内热舒适度等级预测

陈彦如¹, 张涂静娃², 杜千¹, 冉茂亮¹, 王红军³

1. 西南交通大学经济管理学院, 成都 610031;
2. 中铁二院工程集团有限责任公司建筑工程设计研究院, 成都 610031;
3. 西南交通大学信息科学与技术学院, 成都 611756

通讯作者: 王红军
作者简介:陈彦如(1974-),女,内蒙古包头人,教授,博士,主要研究方向:物流配送资源优化、机器学习;张涂静娃(1991-),女,贵州遵义人,工程师,硕士,主要研究方向:通风空调、室内空气品质、室内给排水及消防;杜千(1997-),女,四川成都人,硕士研究生,主要研究方向:物流配送资源优化、数据挖掘;冉茂亮(1997-),女,重庆人,硕士研究生,主要研究方向:机器学习、物流配送资源优化;王红军(1977-),男,四川广安人,副研究员,博士,CCF高级会员,主要研究方向:机器学习、数据挖掘。
基金资助:
国家重点研发计划项目（2018YFC0705000）。

Abstract

Abstract: Since the semi-closed and semi-opened spaces such as high-speed railway station have the indoor thermal comfort level difficult to predict, a Deep Forest (DF)-based deep learning method was proposed to realize the scientific prediction of thermal comfort level. Firstly, the heat exchange environment of high-speed railway station was modeled based on field survey and Energy Plus platform. Secondly, 8 influence factors, such as passenger density, operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, were presented, and 424 operating conditions were designed to obtain massive data. Finally, DF was used to obtain the relationship between thermal comfort and influence factors in order to predict the indoor thermal comfort level of high-speed rail station. Deep Neural Network (DNN) and Support Vector Machine (SVM) were provided as comparison algorithms for the verification. Experimental results show that, among the three models, DF performs best in terms of the prediction accuracy and weighted-F₁, and has the best prediction accuracy of 99.76% and the worst of 98.11%. Therefore, DF can effectively predict the indoor thermal comfort level of high-speed railway stations.

Key words: urban computing, Deep Forest (DF), indoor thermal comfort, dissatisfaction rate of thermal environment, Deep Neural Network (DNN)

摘要： 对于高铁站这类半封闭半开放空间的室内环境热舒适度等级难以准确预测的问题，提出基于深度森林（DF）的深度学习方法对热舒适度等级进行科学预测。首先基于现场调研和Energy Plus平台对高铁站室的热交换环境进行建模；其次提炼出客流密度、多联机开行台数和多联机设置温度等8个影响因素，并设计424种工况以获取海量数据；最后采用DF挖掘热舒适度与影响因素之间的关系，以对高铁站室内热舒适度等级进行预测。采用深度神经网络（DNN）和支持向量机（SVM）作为对比算法进行验证。实验结果表明，在3种模型中，DF在预测正确率和weighted-F₁上表现最佳，DF的预测正确率最高达到99.76%，最低为98.11%。因此，DF能够有效预测高铁站室内的热舒适度等级。

关键词: 城市计算, 深度森林, 室内热舒适度, 热环境不满意率, 深度神经网络

CLC Number:

TP181

CHEN Yanru, ZHANG Tujingwa, DU Qian, RAN Maoliang, WANG Hongjun. Prediction of indoor thermal comfort level of high-speed railway station based on deep forest[J]. Journal of Computer Applications, 2021, 41(1): 258-264.

陈彦如, 张涂静娃, 杜千, 冉茂亮, 王红军. 基于深度森林的高铁站室内热舒适度等级预测[J]. 计算机应用, 2021, 41(1): 258-264.

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Prediction of indoor thermal comfort level of high-speed railway station based on deep forest

基于深度森林的高铁站室内热舒适度等级预测

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