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《Journal of Harbin Institute of Technology》 2006-09
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Hourly water demand forecast model based on least squares support vector machine

CHEN Lei,ZHANG Tu-qiao(1.Municipal Engineering Institute,Zhejiang University,Hangzhou 310027,China)  
As traditional neural network suffers from the problems like the existence of many local minima and the choice of the number of hidden units,and overfiting least squares support vector machine(LSSVM) is proposed to predict the hourly water demand.LSSVM employs the idea of structural risk minimization and VC dimension of the learning machine.In LSSVM,the SVM problem formulation is modified by introducing a least squares cost function and equality instead of inequality constraints,and the solution follows directly from solving a set of linear equations instead of quadratic programming.In the case of radial basis function(RBF) kernel,LSSVM has only two additional tuning parameters,which is less than for standard SVM.According to the periodicity and trend of water demand series,an hourly water demand forecast model based on LSSVM is developed.Case study shows that LSSVM-based hourly water demand forecast model has better generalization ability than BP neural network-based forecast model.
【Fund】: 国家自然科学基金资助项目(50078048)
【CateGory Index】: TU991.31
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