Short Term Load Forecasting Based on the Particle Swarm Optimization with Simulated Annealing

被引：0

作者：

Liu, Mengliang ^{[1
]}

Tang, Jing ^{[1
]}

机构：

[1] Shandong Agr Univ, Sch Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China

来源：

PROCEEDINGS OF THE 6TH CONFERENCE OF BIOMATHEMATICS, VOLS I AND II: ADVANCES ON BIOMATHEMATICS | 2008年

关键词：

short term load forecasting; artificial neural network; particle swarm optimization; simulated annealing;

D O I：

暂无

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper presented an artificial neural network (ANN) method based on the particle swarm optimization (PSO) and simulated annealing (SA) for load forecasting. Using the modified PSO with SA train the ANN network and facilitate the tuning of the optimal network weight and threshold. The ANN network has a better ability to escape from the local optimum and is more effective than the conventional PSO-based ANN. Then use the network to forecast the daily load. Simulation example shows that the proposed approach has good accuracy.

引用

页码：397 / 400

页数：4

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