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Long-Term Power Load Forecast Based on Improved BP Neural Network

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Abstract:

This article proposed the improvement BP algorithm which solved the neural network to restrain slow and easy well to fall into the partial minimum the question, through setup time sequence forecast model made the long-term power forecast, and made a comparison with the traditional BP natural network.

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Periodical:

Key Engineering Materials (Volumes 439-440)

Pages:

848-853

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.439-440.848

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Cite this paper

Online since:

June 2010

Authors:

Shuang Chen Li, Di Yuan

Keywords:

Improved BP Neural Network, Levenberg-Marqardt Algorithm, Long-Term Power Load Forecast

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© 2010 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] Yan-jing SUN, Shen ZHANG, Chang-xin MIAO, Jing-meng LI : Improved BP Neural Network for Transformer Fault Diagnosis Journal of China University of Mining and Technology, Vol. 17(1), March 2007, pp.138-142.

DOI: 10.1016/s1006-1266(07)60029-7

Google Scholar

[2] A. M. Al-Kandari, S. A. Soliman, M. E. El-Hawary. Fuzzy short-term electric load forecasting. International Journal of Electrical Power and Energy Systems, Vol. 26(2), pp.111-122.

DOI: 10.1016/s0142-0615(03)00069-3

Google Scholar

[3] Zhi Xiao, Shi-Jie Ye, Bo Zhong, Cai-Xin Sun: BP neural network with rough set for short term load forecasting Expert Systems with Applications, Vol . 36(1), January 2009, pp.273-279.

DOI: 10.1016/j.eswa.2007.09.031

Google Scholar

[4] K. Kalaitzakis, G. S. Stavrakakis, E. M. Anagnostakis: Short-term load forecasting based on artificial neural networks parallel implementation Electric Power Systems Research, Volume 63(3), October 2002, pp.185-196.

DOI: 10.1016/s0378-7796(02)00123-2

Google Scholar

[5] Bahman Kermanshahi: Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities, Neuro computing, Volume 23(1-3), December 1998, pp.125-133.

DOI: 10.1016/s0925-2312(98)00073-3

Google Scholar