Short-term load forecasting using radial basis function networks

被引：0

作者：

Gontar, Z ^{[1
]}

Sideratos, G

Hatziargyriou, N

机构：

[1] Univ Lodz, Dept Comp Sci, PL-90131 Lodz, Poland

[2] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens, Greece

来源：

METHODS AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2004年 / 3025卷

关键词：

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：

This paper presents results from the application of Radial Basis Function Networks (RBFNs) to Short-Term Load Forecasting. Short-term Load Forecasting is nowadays a crucial function, especially in the operation of liberalized electricity markets, as it affects the economy and security of the system. Actual load series from Crete are used for the evaluation of the developed structures providing results of satisfactory accuracy, retaining the advantages of RBFNs.

引用

页码：432 / 438

页数：7