NEURAL NETWORK BASED SHORT-TERM LOAD FORECASTING

被引：186

作者：

LU, CN ^{[1
]}

WU, HT ^{[1
]}

VEMURI, S ^{[1
]}

机构：

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

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 1993年 / 8卷 / 01期

关键词：

SHORT TERM LOAD FORECASTING; ARTIFICIAL NEURAL NETWORKS; WEATHER SENSITIVE LOAD FORECAST;

D O I：

10.1109/59.221223

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：

The artificial neural network (ANN) technique for short term oad forecasting (STLF) has been proposed by several authors, and gained a Lot of attention recently. In order to evaluate ANN as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF probLems. This paper makes an attempt to address these issues. The paper presents the results of a study to investigate whether the ANN modeL is system dependent, and/or case dependent. Data from two utilities were used in modeling and forecasting. In addition, the effectiveness of a next 24 hour ANN model in predicting 24 hour Load profile at one time was compared with the traditional next one hour ANN model.

引用

页码：336 / 342

页数：7

共 15 条

[1] ADAPTIVE WEATHER-SENSITIVE SHORT-TERM LOAD FORECAST [J].