Fuzzy logic for short term load forecasting

被引：57

作者：

Ranaweera, DK

Hubele, NF

Karady, GG

机构：

[1] Arizona State University, Tempe, AZ

来源：

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS | 1996年 / 18卷 / 04期

关键词：

short term load forecasting; fuzzy logic; learning algorithm; back-propagation network;

D O I：

10.1016/0142-0615(95)00060-7

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 results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back-propagation neural network methods. Copyright (C) 1996 Elsevier Science Ltd.

引用

页码：215 / 222

页数：8

共 16 条

[1]

[Anonymous], 1992, NEURAL NETWORKS FUZZ

[2]

BENTLY H, 1987, FUZZY SETS SYST, V27, P97

[3] WEATHER SENSITIVE SHORT-TERM LOAD FORECASTING USING NONFULLY CONNECTED ARTIFICIAL NEURAL NETWORK [J].