A Short Term Load Forecasting Algorithm Based on Gray Elman Neural Network and Genetic Algorithm

被引：0
|
作者：
Wang, Baoyi [1 ]
Wang, Zheng [1 ]
Zhang, Shaomin [1 ]
机构：
[1] North China Elect Power Univ, Sch Control & Comp Engn, Baoding 071003, Hebei Province, Peoples R China
来源：
PROCEEDINGS OF THE 2015 3RD INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND INFORMATION TECHNOLOGY APPLICATIONS | 2015年 / 35卷
关键词：
gray theory; genetic algorithm; Elman neural network; load forecasting;
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：
Short term power load sample is highly variable, and the influence factors are not determined, the data sample is little. In view of the characteristics of the load, we combine the Grey Theory and Elman neural network to predict the short-term power load. Because the gray neural network convergence is slow, We introduce the genetic algorithm to the gray Elman neural network optimization, and propose the genetic algorithm to optimize the gray Elman neural network algorithm, the genetic algorithm to optimize the gray Elman neural network algorithm is applied to short-term load forecast. Experimental results show that the prediction accuracy is improved. The algorithm achieves fast convergence, and it is feasible and effective.
引用
收藏
页码：526 / 531
页数：6
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