Comparison of very short-term load forecasting techniques

被引：195

作者：

Liu, K

Subbarayan, S

Shoults, RR

Manry, MT

Kwan, C

Lewis, FL

Naccarino, J

机构：

[1] UNIV TEXAS,ENERGY SYST RES CTR,ARLINGTON,TX 76019

[2] NETWORK MANAGEMENT TECHNOL INC,SUGAR LAND,TX 77487

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 1996年 / 11卷 / 02期

基金：

美国国家科学基金会;

关键词：

AGC; load forecast; artificial intelligent;

D O I：

10.1109/59.496169

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：

Three practical techniques - Fuzzy Logic (FL), Neural Networks (NN), and Auto-regressive model (AR) - for very short-term load forecasting have been proposed and discussed in this paper. Their performances are evaluated through a simulation study. The preliminary study shows that it is feasible to design a simple, satisfacttory dynamic forecaster to predict the very short-term load trends on-line. FL and NN can be good candidates for this application.

引用

页码：877 / 882

页数：6

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