基于BP神经网络的地铁站厅空调负荷预测

摘要/Abstract

摘要： 中央空调系统是城市轨道交通系统车站建筑中的重点耗能设备,由于在运营初期其负荷远小于设计负荷、缺乏实时负荷值而无法根据建筑的实际负荷动态调节,导致其目前能耗较大。文中以地铁车站站厅公共区域的空调系统为研究对象,根据空调负荷计算方法,基于trnsys系统仿真平台建立负荷计算模型。按照正交试验方法设计的试验方案,采用仿真模拟的方法对显著影响地铁车站站厅空调能耗的因素进行了研究。基于影响因素的显著性大小排序和BP神经网络理论建立了空调负荷预测模型。以预测负荷值与实际负荷值误差最小作为目标函数,采用仿真模拟实验数据作为训练样本对模型进行训练。训练过程较为稳定,未出现明显震荡(R²=0.99956),预测负荷与模拟负荷的均方根误差变异系数较小(3.6%)。在客流变化、天气变化的情况下对模型进行验证,最大相对误差分别为9.8257%和11.675%。验证结果表明,模型预测精度较高,具有较好的泛化能力,能有效预测地铁车站站厅公共区域空调负荷,可为地铁车站空调控制系统提供依据。

关键词: BP神经网络, 地铁车站, 方差分析, 负荷预测

Abstract: The central air conditioning system is the emphases energy-consuming equipment in the station building of urban rail transit system.In the initial operational stage,there are many reasons,such as the load of station air conditioning is far less than the designed load,the lack of real-time load value and the inability to dynamically adjust accor-ding to the actual load of the building,which lead to the current energy consumption.In this paper,the air conditioning system in public area of metro station hall is taken as the research object.On the basis of the air conditioning load calculation method,load calculation model is established based on TRNSYS system simulation platform.Applying the orthogonal test method to design the test scheme and the simulation method to study the factors that have significant influence on the air conditioning energy consumption of the subway station hall.A load forecasting model for air conditioning system is established based on the significance orders of factors and BP neural network theory.The objective function is to minimize the error between the predicted load and the actual load,and the model is trained by using simulation experimental data as training samples.The training process was relatively stable and there was no obvious shock(R²=0.99956).The variance coefficient of root mean square error between predicted load and simulated load is small(3.6%).The maximum relative errors of the model are 9.8257% and 11.675% respectively when the passenger flow and weather change.The validation results indicate that the model has high prediction accuracy and preferably generalization ability,which is an effective method for air conditioning load forecasting in public area of metro station hall,and can provide basis for air conditioning control system of Metro station.

Key words: BP neutral network, Load forecasting, Metro station, Variance analysis

中图分类号:

TP391

李婷婷, 毕海权, 王宏林, 王晓亮, 周远龙. 基于BP神经网络的地铁站厅空调负荷预测[J]. 计算机科学, 2019, 46(11A): 590-594. https://doi.org/

LI Ting-ting, BI Hai-quan, WANG Hong-lin, WANG Xiao-liang, ZHOU Yuan-long. Prediction of Air-conditioning Load in Metro Station Hall Based on BP Neutral Network[J]. Computer Science, 2019, 46(11A): 590-594. https://doi.org/

参考文献

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