The Method for Short-Term Forecast Electricity Load Based on Criteria Informativeness and Compactness

被引：0

作者：

Gritsay, Aleksandr S. ^{[1
]}

Makarov, Vladimir V. ^{[1
]}

Khamitov, Rustam N. ^{[2
]}

Tatevosyan, Andrey A. ^{[2
]}

Gritsay, Sergey N. ^{[3
]}

机构：

[1] Omsk State Tech Univ, Comp Sci Fac, Omsk, Russia

[2] Omsk State Tech Univ, Elect Engn Fac, Omsk, Russia

[3] Omsk State Rd Management Co, Elect Dept, Omsk, Russia

来源：

PROCEEDINGS OF THE 2019 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (EICONRUS) | 2019年

关键词：

forecast electricity load; rapidminer; method for prepare training set; neural network; NEURAL-NETWORK;

D O I：

10.1109/eiconrus.2019.8657160

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In the article research the method of preparing training samples for building the process of forecast electricity load. FRiS-Stolp function was used to clear the training samples from the noise component and emissions data. The optimal value of the parameter F* for eliminate noise is obtained. The results are presented in the charts. The forecast electricity load error for different values of the coefficient F* is also shown.

引用

页码：527 / 530

页数：4

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