DocumentCode
3108259
Title
Study of Power System Short-term Load Forecast Based on Artificial Neural Network and Genetic Algorithm
Author
Du Xin-hui ; Feng, Tian ; Shao-Qiong, Tan
Author_Institution
Coll. of Electr. & Power Eng., Taiyuan Univ. of Technol., Taiyuan, China
fYear
2010
fDate
26-28 Sept. 2010
Firstpage
725
Lastpage
728
Abstract
The correct schedule, planning and operation of power system has a tight correlation between accurate load forecast. Aims at the variant feature of power system short-term load, the author took a widely study and discussion on the method of artificial neural network applied on power system short-term load forecasting. At the base of three layered BP neural network, the author studied the meteorological factor effect on short-term load forecasting precision, present the short-term load forecasting model made of BP neural network combine with genetic algorithm. According to the load data of area grid and relevant meteorological data, the author forecasted the short-term load with method of three layered BP neural network, four layered BP neural network and four layered BP neural network combine with genetic algorithm. The result shows that four layered BP neural network combine with genetic algorithm have the advantage of fast calculating time and high precision, have value for engineering application and significance for popularization.
Keywords
backpropagation; genetic algorithms; load forecasting; power engineering computing; power system planning; BP neural network; artificial neural network; genetic algorithm; meteorological factor effect; power system operation; power system planning; power system scheduling; power system short-term load forecasting; Artificial neural networks; Biological cells; Load forecasting; Neurons; Weather forecasting; Artificial neural network; BP algorithm; Genetic algorithm; Short-term load forecasting;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Aspects of Social Networks (CASoN), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-8785-1
Type
conf
DOI
10.1109/CASoN.2010.166
Filename
5636945
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