本文基于神经网络技术设计空调控制软件系统，对传统人工控制模式和神经网络控制器进行对比研究.首先利用Energy Plus仿真软件建立真实高铁站建筑及其多联机空调系统模型，对该空调系统设置424种工况完成了一整年运行仿真，然后从百万条仿真数据中抽取PMV （predicted mean vote，预测平均投票）热舒适度和能耗优秀的数据训练神经网络控制器，最后用JavaEE技术开发了该高铁站空调控制软件原型系统并利用Energy Plus仿真数据以及机器学习预测模型模拟实现了空调动态控制.实验结果表明，在冬季和夏季典型工况条件下神经网络控制器比人工固定设置空调参数更加节能.
In this study, air conditioning control software is designed with neural network technology, and the traditional manual control mode and neural network controller are compared. First, Energy Plus is used to build a real high-speed railway station building and its multi-connected air conditioning system, with 424 working conditions of the air conditioning system set up to complete the operation simulation for a whole year. Then the neural network controller is trained with data having excellent predicted mean vote (PMV)-based thermal comfort and energy consumption which are extracted from millions of simulation data. Finally, the prototype system of air conditioning control software for the high-speed railway station is developed with Java Enterprise Edition (JavaEE), and the dynamic control of air conditioners is realized by using Energy Plus simulation data and simulation with a machine learning prediction model. The simulation results based on this prototype software system show that the intelligent controller can reduce energy consumption in comparison with manual control based on fixed settings under typical working conditions in winter and summer.