Short term load forecasting using artificial neural networks for the west of Iran

被引：0
|
作者：
Department of Electrical Engineering, Faculty of Engineering, Razi University, Tagh-e-Bostan, Kermanshah-67149, Iran [1 ]
机构：
[1] Department of Electrical Engineering, Faculty of Engineering, Razi University, Tagh-e-Bostan
来源：
J. Appl. Sci. | 2007年 / 12卷 / 1582-1588期
关键词：
ERNN; Load forecasting; MLP; RBFN;
D O I：
10.3923/jas.2007.1582.1588
中图分类号：
学科分类号：
摘要：
In this study, the use of neural networks to study the design of Short-Term Load Forecasting (STLF) Systems for the west of Iran was explored. The three important architectures of neural networks named Multi Layer Perceptron (MLP), Elman Recurrent Neural Network (ERNN) and Radial Basis Function Network (RBFN) to model STLF systems were used. The results show that RBFN networks have the minimum forecasting error and are the best method to model the STLF systems. © 2007 Asian Network for Scientific Information.
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页码：1582 / 1588
页数：6
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