Short-term load forecasting using neural network with principal component analysis

被引：0
|
作者：
Guo, XC [1 ]
Chen, ZY [1 ]
Ge, HW [1 ]
Liang, YC [1 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
neural networks; principal component analysis; single parameter dynamic search algorithm (SPDS); short-term load forecasting;
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：
A neural-network-based approach for short-term load forecasting of electrical power is proposed. The principal component analysis (PCA) technique is used to reduce the original electric load variables to several characteristic variables. A single parameter dynamic search algorithm (SPDS) is employed to train the neural network. Since the training sample sets can be chosen before forecasting, the interference of the non-correlative samples for the forecasting can be avoided. The effectiveness and the feasibility of on line forecasting of the proposed method are examined using simulated experiments.
引用
收藏
页码：3365 / 3369
页数：5
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