Urban water demand forecasting by LS-SVM with tuning based on elitist teaching-learning-based optimization

Author

Gang Ji ; Jingcheng Wang ; Yang Ge ; Huajiang Liu

Author_Institution

Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China

fYear

2014

fDate

May 31 2014-June 2 2014

Firstpage

3997

Lastpage

4002

Abstract

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.

Keywords

learning (artificial intelligence); least squares approximations; optimisation; regression analysis; support vector machines; water resources; water supply; ATLBO; China; LS-SVM; PSO; Shanghai; ameliorated TLBO algorithm; elitist teaching-learning-based optimization; forecast accuracy; grid search; hourly water demand forecasting performance; hyperparameter adjustment; least squares support vector machine; particle swarm optimization; regression precision; tuning; urban water demand forecasting; water supply system; Accuracy; Demand forecasting; Optimization; Predictive models; Support vector machines; Training; Tuning; ATLBO; LS-SVM; Water Demand Forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (2014 CCDC), The 26th Chinese

Conference_Location

Changsha

Print_ISBN

978-1-4799-3707-3

Type

conf

DOI

10.1109/CCDC.2014.6852880

Filename

6852880