The Short-term Load Forecasting by Applying the Fuzzy Neural Net

被引：0
|
作者：
Wang Xiao-Wen [1 ]
Fu Xuan [2 ]
Sun Xiao-Yu [3 ]
Wu Zhi-Hong [4 ]
机构：
[1] Shenyang Inst Engn, Coll Renewable Energy, Shenyang, Peoples R China
[2] Shenyang Inst Engn, Coll Informat, Shenyang, Peoples R China
[3] Shenyang Inst Engn, Coll Int Educ, Shenyang, Peoples R China
[4] Shenyang Inst Engn, Coll Elect Power, Shenyang, Peoples R China
来源：
2013 6TH INTERNATIONAL CONFERENCE ON INTELLIGENT NETWORKS AND INTELLIGENT SYSTEMS (ICINIS) | 2013年
关键词：
Artificial neural net; Fuzzy system; Load forecasting;
D O I：
10.1109/ICINIS.2013.52
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The fuzzy system used for short-term load forecasting is put forward. This system, possesses the structure of neural net and learning algorithm, addressed as fuzzy neural net FNN. FNN generates the rules with the existing history loads and supplements the rules with minimum membership method. After the parameters of rule have been amended, the output of FNN can be well coincident with the data of loads. Once being trained, FNN can forecast future loads right away.
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页码：178 / 180
页数：3
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