Based on Dynamic Recursion of Fuzzy Neural Network in the Short-term Load Forecasting of Power System

被引：0
|
作者：
Wang, Lei [1 ]
Lun, Zhixin [2 ]
Cao, Xiushuang [1 ]
机构：
[1] Tangshan Coll, Dept Informat Engn, Tang Shan 063000, Peoples R China
[2] Comp Ctr Tangshan Coll, Tang Shan 063000, Peoples R China
来源：
2010 INTERNATIONAL CONFERENCE ON INFORMATION, ELECTRONIC AND COMPUTER SCIENCE, VOLS 1-3 | 2010年
关键词：
Dynamic recursion; Fuzzy neural network; Forecasting model; Short-term load; GENETIC ALGORITHMS; RECURRENT; IDENTIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the model the power system short-term load forecasting, a dynamic recurrent fuzzy neural network (DRFNN) is proposed. Introducing local recurrent units to hidden layer. And the fuzzy inference function is realized easily by using a product operation in the network. The performance of the model proposed is evaluated, based on a Sorth China power grid operational load data. Simulation results showed that the improved algorithm could gain better forecasting effect.
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页码：336 / +
页数：3
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