A novel approach to short-term load forecasting using fuzzy neural networks

被引：96

作者：

Papadakis, SE ^{[1
]}

Theocharis, JB ^{[1
]}

Kiartzis, SJ ^{[1
]}

Bakirtzis, AG ^{[1
]}

机构：

[1] Aristotelian Univ Salonika, Dept Elect & Comp Engn, GR-54006 Thessalonika, Greece

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 1998年 / 13卷 / 02期

关键词：

fuzzy neural networks; short-term load forecasting;

D O I：

10.1109/59.667372

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：

An efficient modeling technique based on the fuzzy curve nation is developed in this paper to generate fuzzy models for short term load forecasting. The suggested forecasting approach proceeds on the following steps: a) prediction of the load curve extremals (peak and valley loads) using separate fuzzy models, b) formulation of the representative day based on historical load data and c) mapping of the representative day load curve to the forecasted peak values to obtain the predicted day load curves. A very good prediction performance is attained as shown in the simulation results which verify the effectiveness of the modeling technique.

引用

页码：480 / 489

页数：10

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