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基于图深度学习的供水管网短期需水量预测研究

林昱道 陶涛 信昆仑 蒲政衡 陈磊

林昱道, 陶涛, 信昆仑, 蒲政衡, 陈磊. 基于图深度学习的供水管网短期需水量预测研究[J]. 环境工程, 2023, 41(4): 149-153. doi: 10.13205/j.hjgc.202304021
引用本文: 林昱道, 陶涛, 信昆仑, 蒲政衡, 陈磊. 基于图深度学习的供水管网短期需水量预测研究[J]. 环境工程, 2023, 41(4): 149-153. doi: 10.13205/j.hjgc.202304021
LIN Yudao, TAO Tao, XIN Kunlun, PU Zhengheng, CHEN Lei. GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 149-153. doi: 10.13205/j.hjgc.202304021
Citation: LIN Yudao, TAO Tao, XIN Kunlun, PU Zhengheng, CHEN Lei. GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 149-153. doi: 10.13205/j.hjgc.202304021

基于图深度学习的供水管网短期需水量预测研究

doi: 10.13205/j.hjgc.202304021
详细信息
    作者简介:

    林昱道(1998-),男,硕士研究生,主要研究方向为给排水管网智能调度。linyudao2010@hotmail.com

    通讯作者:

    陶涛(1974-),女,教授,主要研究方向为城市水资源与给排水管网设计运行最优化。taotao@tongji.edu.cn

GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK

  • 摘要: 供水系统短期需水量预测是供水管网优化调度的基础,预测的准确性对供水调度有重要影响。图神经网络是一种新型神经网络架构,通过学习对象的拓扑特征优化学习效果。利用供水管网的拓扑特征搭建了图波网络(Graph Wavenet)模型,用于学习供水管网各监测点的空间拓扑关系及其需水量时间序列关系,实现了更准确的供水管网短期需水量预测。使用真实管网历史数据训练Graph Wave Net模型,未来5 min预测的最小百分比误差达到1.28%,平均百分比误差达到1.34%。实验成功地将给水管网拓扑结构用于优化深度学习模型效果,使用图卷积网络模型有效提升了供水管网短期需水量预测精度。并对图神经网络在供水管网领域的应用前景进行了简要探讨。
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出版历程
  • 收稿日期:  2022-04-21
  • 网络出版日期:  2023-05-26
  • 刊出日期:  2023-04-01

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