SHORT-TERM LOAD FORECASTING USING AN ARTIFICIAL NEURAL NETWORK

被引：279
|
作者：
LEE, KY
CHA, YT
PARK, JH
KURZYN, MS
PARK, DC
MOHAMMED, OA
机构：
[1] PUSAN NATL UNIV,DEPT ELECT ENGN,PUSAN 609735,SOUTH KOREA
[2] FLORIDA INT UNIV,MIAMI,FL 33199
[3] VICTORIA COLL,CLAYTON,VIC,AUSTRALIA
来源：
IEEE TRANSACTIONS ON POWER SYSTEMS | 1992年 / 7卷 / 01期
关键词：
NEURAL NETWORK; LOAD FORECASTING; BACKPROPAGATION ALGORITHM;
D O I：
10.1109/59.141695
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：
Artificial Neural Network (ANN) Method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern include Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of ANN for short-term load forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers are tested with various combination of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives good load forecast.
引用
收藏
页码：124 / 132
页数：9
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