二次供水流量预测模型的构建和应用
Construction and Application of Flow Prediction Model in Secondary Water Supply
肖磊 1蒋瑜 2刘书明 3吴雪 3陈春芳2
作者信息
- 1. 清华大学环境学院,北京 100084;常州通用自来水有限公司,江苏常州 213003
- 2. 常州通用自来水有限公司,江苏常州 213003
- 3. 清华大学环境学院,北京 100084
- 折叠
摘要
目前二次供水系统中水泵普遍存在"大马拉小车"导致能耗偏高的问题,为了解决这一问题,需要建立一种更加贴合实际工况的二次供水流量预测方法.文章以江苏常州地区 149 个二次供水小区的流量监测数据为基础,形成二次供水流量特征样本集,首次综合增压户数、入住率、最高日流量、最高日最大时流量等特征数据,依靠基于遗传算法优化的BP神经网络进行数据挖掘,构建具备可用性的二次供水流量预测模型,并应用于老旧泵房的水泵改造工作,电耗降幅达 19%以上,取得了良好的节能效果.流量预测模型可以为二次供水设计选型、节能改造等工作提供更精准的流量评估工具,也为二次供水节能减排提供新的研究思路,助力实现"双碳"目标,推进供水绿色发展.
Abstract
At present,the water pump in the secondary water supply system generally has the problem of high energy consumption caused by"big horse pulls a small carriage".In order to solve the problem,a kind of secondary water supply flow prediction model which is more suitable for the actual working conditions need to be established.Based on the flow monitoring data of 149 secondary water supply communities in Changzhou,Jiangsu Province,this paper formed a characteristic sample set of secondary water supply flow.It was the first time that the characteristic data such as the number of pressurized households,the occupancy rate,the highest daily flow rate and the maximum hourly flow rate on the highest flow rate day were integrated,and the BP neural network optimized based on genetic algorithm was used for data mining.The prediction model of secondary water supply flow with availability was constructed and applied to the renovation of old pump house.Then power consumption had decreased by over 19%.Good energy saving effect was achieved.Flow prediction model can provide more accurate flow evaluation tools for secondary water supply design selection,energy saving renovation and other work,but also provide new research ideas for secondary water supply energy saving and emission reduction,help achieve the"double carbon"goal,and promote the green development of water supply.
关键词
二次供水/遗传算法/BP神经网络/流量预测/节能减排Key words
secondary water supply/genetic algorithm/BP neural network/flow prediction/energy saving and emission reduction引用本文复制引用
基金项目
水体污染控制与治理科技重大专项(2017ZX07201002)
出版年
2024
国家科技期刊平台
NSTL
万方数据