基于LightGBM的水厂供水压力预测  

FORECASTING OF WATER SUPPLY PRESSURE BASED ON LIGHTGBM

作　　者：耿为民 颜军 张典 马平川 阳国华 Geng Weimin;Yan Jun;Zhang Dian;Ma Pingchuan;Yang Guohua(Municipal and Ecological Engineering School,Shanghai Urban Construction Vocational College,Shanghai 200438,China;Shandong Water Land Co.,Ltd.,Zaozhuang 277101,Shandong,China;Clinbrain Co.,Ltd.,Shanghai 200233,China;Department of Information and Intelligent Engineering,Shanghai Publishing and Printing College,Shanghai 200093,China;Shanghai Institute of Computing Technology Co.,Ltd.,Shanghai 200040,China)

机构地区：[1]上海城建职业学院市政与生态工程学院,上海200438 [2]山东沃特兰德环境科技有限公司,山东枣庄277101 [3]上海柯林布瑞信息技术有限公司,上海200233 [4]上海出版印刷高等专科学校信息与智能工程系,上海200093 [5]上海市计算技术研究所有限公司,上海200040

出　　处：《计算机应用与软件》2024年第4期340-343,349,共5页Computer Applications and Software

基　　金：上海市住房和城乡建设管理委员会科研项目(沪建科2021002056)。

摘　　要：针对城市供水管网调度问题,提出一种基于LightGBM(Light Gradient Boosting Machine)的水厂供水压力预测模型。对压力监测点历史数据提取时间特征,并根据特征重要性对测压点排序,以特征权重筛选、特征权重与经验相结合两种方式选取控制点。以南方某城市供水系统为算例,结果表明采用特征权重分析、人工经验相结合选用控制点来预测,具有较高和稳定的预测精度。Aimed at the scheduling problem of urban water distribution system,a water supply pressure prediction model based on LightGBM(Light Gradient Boosting Machine)is proposed.The time characteristics of the historical data on pressure monitoring points were extracted.The monitoring points were sorted according to the feature importance.The control points were selected in two ways:one was according to feature weight,and the other one was combined feature weight and experience.Taking a water supply system in southern China as a research case,the results show it has high and stable prediction accuracy that the control points are selected by combining feature weight analysis and scheduling experience.

关 键 词：供水系统 压力预测 特征权重 LightGBM 

分 类 号：TU991[建筑科学—市政工程]