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基于注意力机制的CNN-BiLSTM模型在地铁站公共区空调冷负荷预测中的应用

姚明阳 郭红红 马汉林

节能2025,Vol.44Issue(2):61-65,5.
节能2025,Vol.44Issue(2):61-65,5.DOI:10.3969/j.issn.1004-7948.2025.02.016

基于注意力机制的CNN-BiLSTM模型在地铁站公共区空调冷负荷预测中的应用

Application of CNN-BiLSTM model based on attention mechanism in prediction of cooling load of air conditioning in public area of subway station

姚明阳 1郭红红 2马汉林1

作者信息

  • 1. 柳州铁道职业技术学院城轨交通与信息学院,广西 柳州 545000
  • 2. 成都地铁运营有限公司,四川 成都 610000
  • 折叠

摘要

Abstract

The accuracy of air conditioning cooling load prediction is of great significance to realize the real-time control and energy-saving operation of air conditioning system.A CNN-BiLSTM model based on attention mechanism was proposed to predict the cooling load of air conditioning in the public area of a subway station in Chengdu,and the correlation degree of the influence of meteorological parameters,passenger flow and other parameters on the current cooling load was analyzed.Combining the feature extraction capability of convolutional neural network(CNN),the time series processing capability of bidirectional long short-term memory network(BiLSTM)and the important feature attention capability of attention mechanism,a cold load prediction model is established.Compared with BP neural network,long short Term memory network(LSTM)and convolutional Long Short Term memory network(CNN-LSTM),the prediction effect was compared.The results show that CNN-BiLSTM based on attention mechanism has higher accuracy and reliability.Compared with BP model,RMSE and MAE of CNN-ATT-BiLSTM model decreased by 70.78%and 69.56%,respectively,and R2 increased by 23.48%.Compared with LSTM model,RMSE and MAE of CNN-ATT-BiLSTM model decreased by 62.54%and 61.62%,respectively,and R2 increased by 15.71%.Compared with CNN-LSTM model,RMSE and MAE of CNN-ATT-BiLSTM model decreased by 53.13%and 56.21%,respectively,and R2 increased by 10.90%.

关键词

冷负荷/卷积神经网络/双向长短期记忆网络/注意力机制

Key words

cooling load/convolutional neural network/bidirectional long short-term memory network/attention mechanism

分类

交通工程

引用本文复制引用

姚明阳,郭红红,马汉林..基于注意力机制的CNN-BiLSTM模型在地铁站公共区空调冷负荷预测中的应用[J].节能,2025,44(2):61-65,5.

基金项目

2022年广西高校中青年教师科研基础能力提升项目(项目编号:2022KY1410) (项目编号:2022KY1410)

2021年度柳州铁道职业技术学院校级课题(项目编号:2021-KJA04) (项目编号:2021-KJA04)

2025年广西高校中青年教师科研基础能力提升项目(项目编号:2025KY1594) (项目编号:2025KY1594)

节能

1004-7948

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