Fuzzy neural very-short-term load forecasting based on chaotic dynamics reconstruction

被引：0
|
作者：
Yang, HY [1 ]
Ye, H [1 ]
Wang, GZ [1 ]
Hu, TF [1 ]
机构：
[1] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS | 2005年 / 3498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 proposes an improved fuzzy neural system for very-short-term load forecasting problem, based on chaotic dynamics reconstruction techniques. The Grassberger-Procaccia algorithm and the Least Squares Regression method were applied to obtain the accurate value for the correlation dimension, which is used as an estimation of the model order. Based on that model order, an appropriately structured Fuzzy Neural System (FNS) for load forecasting was designed. Satisfactory experimental results were obtained in 15 minutes ahead electrical load forecasting from the electric utility in Shandong Heze area. And the same experiments using conventional Artificial Neural Network are also performed as a comparison with the proposed approach.
引用
收藏
页码：622 / 627
页数：6
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