WEATHER SENSITIVE SHORT-TERM LOAD FORECASTING USING NONFULLY CONNECTED ARTIFICIAL NEURAL NETWORK

被引：98

作者：

CHEN, ST

YU, DC

MOGHADDAMJO, AR

LU, CN

VEMURI, S

机构：

[1] NATL SUN YAT SEN UNIV,KAOHSIUNG,TAIWAN

[2] HARRIS CORP,DIV CONTROLS & COMPOSIT,MELBOURNE,FL 32901

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 1992年 / 7卷 / 03期

关键词：

ARTIFICIAL NEURAL NETWORK (ANN); WEATHER SENSITIVE; SHORT-TERM LOAD FORECASTING;

D O I：

10.1109/59.207323

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：

This paper presents an Artificial Neural Network (ANN) model for forecasting weather sensitive loads. The proposed model is capable of forecasting the hourly loads for an entire week. The model is not fully connected; hence, it has a shorter training time than the fully-connected ANN. The proposed model can differentiate between the weekday loads and the weekend loads. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data. The average percentage peak error for the test cases is 1.12%.

引用

页码：1098 / 1105

页数：8

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