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