Fuzzy neural network and fuzzy expert system for load forecasting

被引：32

作者：

Dash, PK

Liew, AC

Rahman, S

机构：

[1] NATL UNIV SINGAPORE, DEPT ELECT ENGN, SINGAPORE 117548, SINGAPORE

[2] VIRGINIA POLYTECH INST & STATE UNIV, DEPT ELECT ENGN, BLACKSBURG, VA 24061 USA

来源：

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION | 1996年 / 143卷 / 01期

关键词：

neural networks; load forecasting; expert systems; power system planning;

D O I：

10.1049/ip-gtd:19960314

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：

A hybrid neural network fuzzy expert system is developed to forecast short-term electric load accurately. The fuzzy membership values of the load and other weather variables are the inputs to the neural network, and the output comprises the membership values of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. Extensive studies have been performed for all seasons, and a few examples are presented in the paper, including average, peak and hourly load forecasts.

引用

页码：106 / 114

页数：9

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