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
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页数：5
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