Urban Water Demand Forecasting by LS-SVM with Tuning based on Elitist Teaching-Learning-based Optimization

摘要：

　　This paper mainly studies the hourly water demand forecasting performances of water supply system in shanghai with LS-SVM.The teaching-learning-based optimization(TLBO)is adopted to adjust the hyper-parameters of least squares support vector machine(LS-SVM).To improve the forecast accuracy,An ameliorated TLBO algorithm called ATLBO is introduced.The experimental results show that the model of water demand forecasting with ATLBO has better regression precision than grid search,particle swarm optimization(PSO)and TLBO.

关键词： Water Demand Forecasting LS-SVM ATLBO

作者: Gang Ji Jingcheng Wang Yang Ge Huajiang Liu

作者单位: Department of Automation,Shanghai Jiao Tong University,and key laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai,200240

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 3997-4002

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）