Short Term Load Forecasting: A Dynamic Neural Network Based Genetic Algorithm Optimization

被引：0

作者：

Wang, Yan ^{[1
]}

Ojleska, Vesna ^{[2
]}

Jing, Yuanwei ^{[3
]}

K.-Gugulovska, Tatjana ^{[2
]}

Dimirovski, Georgi M. ^{[2
,4
]}

机构：

[1] Northeastern Univ, Shenyang, Peoples R China

[2] SS Cyril & Methodius Univ, Fac EEIT, Skopje, North Macedonia

[3] NE Univ, Coll Informat Sci Engn, Shenyang, Peoples R China

[4] Dogus Univ, Comp Eng & Control Eng, Istanbul, Turkey

来源：

PROCEEDINGS OF 14TH INTERNATIONAL POWER ELECTRONICS AND MOTION CONTROL CONFERENCE (EPE-PEMC 2010) | 2010年

关键词：

Distribution of electrical energy; emerging technology; energy system management; genetic algorithm; modeling; neural network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The short term load forecasting plays a significant role in the management of power system supply for countries and regions, in particular in cases of insufficient electric energy for increased needs. A back-propagation artificial neural-network (BP-ANN) genetic algorithm (GA) based optimizing technique for improved accuracy of predictions short term loads is proposed. With GA's optimizing and BP-ANN's dynamic capacity, the weighted GA optimization is realized by selection, crossing and mutation operations. The performance of the proposed technique has been tested using load time-series from a real-world electrical power system. Its prediction has been compared to those of obtained by only back-propagation based neural-network techniques. Simulation results demonstrated that the here proposed technique possesses superior performance thus guarantees better forecasting

引用

页数：5

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