DocumentCode :
160390
Title :
Application of BP Neural Networks based on genetic simulated annealing algorithm for shortterm electricity price forecasting
Author :
Jun Chen ; Li He ; Yi Quan ; Wang Jiang
Author_Institution :
Sch. of Electr. & Electron. Eng., Hubei Univ. of Technol., Wuhan, China
fYear :
2014
fDate :
9-11 Jan. 2014
Firstpage :
1
Lastpage :
6
Abstract :
BP Neural Network can forecast short-term electricity price, while it is necessary to explore technique to tune the back propagation learning algorithm either for better generalization, or for faster training. The paper proposed enhanced BP Neural Network to forecast electricity price, in which we replaced back propagation algorithm of BP Network with genetic simulated annealing algorithm (GSAA). It integrated GA´s search performance and SA´s strong local search performance, and has a better performance in terms of solution accuracy and convergence speed. Finally, a case study of New South Wales in Australia illustrates the feasibility and effectiveness of the proposed method.
Keywords :
backpropagation; genetic algorithms; load forecasting; power engineering computing; pricing; simulated annealing; Australia; BP neural network; GSAA; New South Wales; back propagation learning algorithm; genetic simulated annealing algorithm; short-term electricity price forecasting; Electricity; Forecasting; Genetics; Neural networks; Predictive models; Simulated annealing; Sociology; BP Neural Network; Genetic simulated annealing algorithm (GSAA); Price forecasting; Weight adjustment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Electrical Engineering (ICAEE), 2014 International Conference on
Conference_Location :
Vellore
Type :
conf
DOI :
10.1109/ICAEE.2014.6838562
Filename :
6838562
Link To Document :
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