面向高铁站的热舒适度和能耗综合预测

针对高铁站这类半封闭建筑的热舒适度影响因素众多，影响机制复杂以及热舒适度与能耗存在背反等问题，提出了基于机器学习的高铁站热舒适度与能耗综合预测方法。首先采用传感器数据捕获及Energy Plus仿真两种方式对高铁站室内外状态、多联机及热交换机等控制单元及热能传导环境进行建模；其次提出影响高铁站热舒适度的八类因素——多联机开启台数、多联机设置温度、热交换机开启台数、客流密度、室外温度、室内温度、室内湿度和室内二氧化碳浓度，并设计424种模型运行工况以及3 714 240个实例；最后设计6种机器学习模型——深度神经网络、支持向量回归、决策树回归、线性回归、岭回归和贝叶斯岭回归，来对高铁站室内热舒适度和空调能耗进行有效预测。实验结果表明，6种机器学习模型中决策树回归预测模型能够在较短的时间内获得最优的预测性能，其平均均方误差低至0.002 2。所得研究成果可直接为下一阶段的温控策略提供主动预判的环境状态参数并实现实时决策。

Comprehensive prediction of thermal comfort and energy consumption for high-speed railway stations

As many factors affect the thermal comfort of semi-enclosed buildings such as high-speed railway stations in a complex way and there exists contradiction between thermal comfort and energy consumption,a comprehensive prediction method for thermal comfort and energy consumption of high-speed railway station based on machine learning was proposed.Firstly,with sensor data capturing and Energy Plus platform,the indoor and outdoor status,the control units like multievaporator air conditioners and heat exchangers as well as the thermal energy transmission environment of high-speed railway station were modeled.Secondly,eight factors influencing the thermal comfort of high-speed railway station,such as the operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners,the operating number of heat exchangers,passenger density,outdoor temperature,indoor temperature,indoor humidity,and indoor carbon dioxide concentration,were proposed,424 model operating conditions and 3714240 instances were designed.Finally,in order to effectively predict indoor thermal comfort and energy consumption of high-speed railway station,six machine learning methods,which are deep neural network,support vector regression,decision tree regression,linear regression,ridge regression and Bayesian ridge regression,were designed.Experimental results show that decision tree regression has the best prediction performance in a short time with average mean squared error of 0.0022.The obtained research results can directly provide actively predicted environmental parameters and realize real-time decision-making for the temperature control strategy in the next stage.