Neural network-based short term load forecasting for unit commitment scheduling

被引：0

作者：

Methaprayoon, K ^{[1
]}

Lee, WJ ^{[1
]}

Didsayabutra, P ^{[1
]}

Liao, J ^{[1
]}

Ross, R ^{[1
]}

机构：

[1] Univ Texas, Energy Syst Res Ctr, Arlington, TX 76109 USA

来源：

2003 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS TECHNICAL CONFERENCE, CONFERENCE RECORD | 2003年

关键词：

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Today's electric power industry is undergoing many fundamental changes due to the process of deregulation. In the new market environment, the power system operation will become more competitive. The utilities are required to perform optimal planning in order to operate their system efficiently. The accuracy of future load forecast becomes crucial. This paper presents the development of an Artificial Neural Network-based short-term load forecasting (STLF) for unit commitment scheduling and resource planning. The network structures are carefully tuned to obtain satisfying forecast results according to the load characteristics of the target utility system. The result indicates that ANN forecaster provides more accurate result and can be modified to satisfy the target utility's requirement.

引用

页码：138 / 143

页数：6

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