A Short-term Load Forecasting Based On Fuzzy Identification In Power System

被引:0
作者
Liang Yu [1 ]
Wang Na [1 ,2 ]
Fan Li-ping [1 ]
机构
[1] Shenyang Univ Chem Technol, Sch Informat Engn, Shenyang, Peoples R China
[2] Shenyang Univ Technol, Shenyang, Peoples R China
来源
2011 INTERNATIONAL CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND AUTOMATION (CCCA 2011), VOL III | 2010年
关键词
Short-term Load Forecastinge; minimal two-multiplication; Fuzzy Identification; ANN; NEURAL-NETWORK;
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
Due to the industrial structure adjustment, the change of resident consumption ability and pattern of consumption, and market-oriented and so on, Short-term power load forecasting for urban plans faces considerable difficulties. Aiming at the limitation of short-term load forecasting technique for power system under the particular circumstance, we presents the methods of classifying load by using fuzzy pattern identify theory, then constructs all kinds of ANN according to class. The method is applied to a short-term load forecasting, and guaranteed exactness and stability of the results. In this paper, a neural network prediction model was established, the example in Heilongjiang was given to validate the accuracy of the algorithm.
引用
收藏
页码:197 / 199
页数:3
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