The Short-term Load Forecasting Based on Grey Theory and RBF Neural Network

被引：0
|
作者：
Li Xiao-cong [1 ]
Wang Le [1 ]
Li Qiu-wen [1 ]
Wang Ke [2 ]
机构：
[1] Guangxi Univ, Sch Elect Engn, Nanning, Peoples R China
[2] Guangxi Power Grid Corp, Nanning Power Supply Bur, Nanning, Peoples R China
来源：
2011 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC) | 2011年
基金：
中国国家自然科学基金;
关键词：
words-Short-term load forecasting; Grey Theory; RBF neural networks;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Interpolation method used to process the raw data, select Grey Theory and RBF (Radial Basis Function) neural network to forecast load. By comparing the accuracy of Grey Theory and RBF neural network forecasting, the results illustrate that the forecasting accuracy are satisfactory; accordingly it shows the validity and practicability of the methods.
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页数：4
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