Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 590-594.

• Interdiscipline & Application • Previous Articles     Next Articles

Prediction of Air-conditioning Load in Metro Station Hall Based on BP Neutral Network

LI Ting-ting1, BI Hai-quan1, WANG Hong-lin1, WANG Xiao-liang2, ZHOU Yuan-long1   

  1. (School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)1;
    (Architecture and Urban Planning College,Southwest Minzu University,Chengdu 610041,China)2
  • Online:2019-11-10 Published:2019-11-20

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

CLC Number: 

  • TP391
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