Research on a genetic neural artificial network in short term load forecasting

被引：0

作者：

Wang Luchao ^{[1
]}

Deng Yongping ^{[1
]}

机构：

[1] Wuhan Univ, Water Resource & Hydropower Coll, Wuhan 430072, Hubei Province, Peoples R China

来源：

ICCSE'2006: Proceedings of the First International Conference on Computer Science & Education: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION | 2006年

关键词：

short-term load forecasting; the Genetic Neural Artificial Network; the activation function; the momentim item;

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：

Short-term load forecasting is one of the most important contents of running and dispatching power system. In order to avoid the limitation of the BP neural networks and improve the efficiency and the accuracy of forecasting,this paper established the short-term load forecasting based on the Genetic Neural Artificial Network. The model mended the activation function, introduced the momentim item and made use of GA to confirm the parameters of the networks. The example showed that this model can effectively improve the forecasting precision.

引用

页码：823 / 825

页数：3

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