Study of Short-Term Load Forecasting of Water Supply System Based on RBF Neural Networks

被引：0

作者：

Zhao, Hong ^{[1
]}

机构：

[1] China Jiliang Univ, Sch Mech Engn, Hangzhou, Zhejiang, Peoples R China

来源：

2011 INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND NEURAL COMPUTING (FSNC 2011), VOL IV | 2011年

关键词：

water supply system; water consumption; RBF neural network; nonlinear time series; short-term load forecasting; modeling;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In view of random and nonlinear peculiarity of municipal short-term water consumption time series, based on Radial Basis Function (RBF) neural networks, the short-term load forecasting model of water supply system is established in this paper. Then it is respectively used in forecasting for daily water consumption and hourly water consumption, simulations show that this method is simple and convenient for use, have high accuracy of prediction, and meets the needs of actual projects.

引用

页码：30 / 33

页数：4