二次供水流量预测模型的构建和应用OACSTPCD

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.