Research on short-term power load forecasting method based on IFOA-GRNN

被引：0

作者：

Zhu X. ^{[1
]}

机构：

[1] Department of Equipment Engineering, Henan Technical College of Construction, Zhengzhou

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 09期

关键词：

Fruit fly optimization algorithm; Generalized regression neural network; Power load forecasting; Smoothing factor;

D O I：

10.19783/j.cnki.pspc.190760

中图分类号：

学科分类号：

摘要：

Examining the problems of strong load randomness, poor short-term load forecasting accuracy and long calculation time in intelligent power environment, a forecasting method combining the improved Drosophila optimization algorithm IFOA and generalized regression neural network GRNN is proposed. The input factors of the model are load data and meteorological information. By improving the search distance of the fruit fly optimization algorithm, the model can be used to optimize the smooth factor of the generalized regression neural network GRNN, thereby improving the performance and prediction accuracy of the network. The accuracy and validity of the proposed prediction method are verified by simulation. The results show that the improved method can reduce prediction error and increase the stability of the algorithm. This study provides a reference for the development of a short-term power load forecasting system in China. © 2020, Power System Protection and Control Press. All right reserved.

引用

页码：121 / 127

页数：6

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