Water demand forecast model of Least Squares Support Vector Machine based on Particle Swarm Optimization

摘要：

　　In order to solve the problem of precision of water demand forecast model,a coupled water demand forecast model of particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) are proposed in this paper.A PSO-LSSVM model based on parameter optimization was constructed in a coastal area of Binhai,Jiangsu Province,and the total water demand in 2009 and 2010 were simulated and forecasted with the absolute value of the relative errors less than 2.1%.The results showed that the model had good simulation effect and strong generalization performance,and can be widely used to solve the problem of small- sample,nonlinear and high dimensional water demand forecast.

作者: Kun Yan Min-Zhi Yang

作者单位: Zhejiang Institute of Hydraulics & Estuary,No.50,Fengqi East Road,Hangzhou 310020,China College of Hydrology and Water Resources,Hohai University,No.1,Xikang Road,Nanjing 210098,China

会议类型: 国际会议

会议名称: 2018 International Symposium on Water System Operations(ISWRSO 2018)(2018年水资源系统及调度国际研讨会)

会议地点: 北京

会议语种:英文

页码: 1-9

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）