Middle anatolian region short-term load forecasting using artificial neural networks

被引：8

作者：

Demiroren, A ^{[1
]}

Ceylan, G ^{[1
]}

机构：

[1] Istanbul Tech Univ, Elect & Elect Fac, Dept Elect Engn, Istanbul, Turkey

来源：

ELECTRIC POWER COMPONENTS AND SYSTEMS | 2006年 / 34卷 / 06期

关键词：

short-term load forecasting; artificial neural networks; similarity based load forecasting;

D O I：

10.1080/15325000500419284

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 recent years, several studies of short-term load forecasting using different of artificial neural network structures have been reported. In this paper, an application of short-term load forecasting is investigated by multilayer perceptron structure. Actual load and temperature data of the Middle Anatolian Region in the years 2002 and 2003 are used for this investigation. In this study, maximum temperature, minimum temperature, and day type factors are used to construct the forecasting model. Also, load forecasting for the same region is obtained by the regression method to compare the effectiveness of the artificial neural network method.

引用

页码：707 / 724

页数：18

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