The Forecast of Energy Demand on Artificial Neural Network

被引：5

作者：

Wang Jin-ming ^{[1
]}

Liang Xin-heng ^{[1
]}

机构：

[1] N China Elect Power Univ, Baoding, Peoples R China

来源：

2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL III, PROCEEDINGS | 2009年

关键词：

energy demand forecast; neural network; nerve cell of hide layer; MATLAB; CONSUMPTION; CHINA;

D O I：

10.1109/AICI.2009.93

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Traditional method about forecast of energy demand, Trend Extrapolation, can't study the information supplied with date effectively, and BP neural network has the great power of goal learning, which can dig potential function in the date. The article design the GDP and other factors as input variables, and use steepest descent back propagation to adjust the weight and threshold of network. We choose the optimal number of hide layer via experimentation, and achieve the train and simulate of network with MATLAB. The final result shows that the forecast of neural network has much higher precision than the forecast of trend extrapolation. The article indicates that BP neural network has the higher precision.

引用

页码：31 / 35

页数：5

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