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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2017 Vol.7(11): 870-875 ISSN: 2010-3689
doi: 10.18178/ijiet.2017.7.11.987

Forecast Model of Coal Demand Based on Improved Tandem Gray BP Neural Network

Guohua Gou
Abstract—In this study, we build a new coal demand prediction model of tandem gray BP neural network. Firstly we use 2000-2015 years coal demand data to establish three gray prediction models: GM(1,1), WPGM(1,1) and pGM(1,1); Secondly, by comparison, we select the best prediction model pGM(1,1) and at the same time take coal demand factors as the BP neural network input, 200-2015year of coal demand date for training and testing. Lastly we proceed to predict coal demand in China in 2016 and 2020. Prediction result is: mean relative error of the improved tandem gray BP neural network prediction results is 1.92%, which is lower 0.158% than pGM(1, 1) model and 0.28% than BP neural network model respectively.

Index Terms—BP neural network, gray forecast, coal demand forecast, tandem gray BP neural network.

Guohua Guo is with the Department of Modern Management, Zhengzhou Technical College, P. C. 450121 China (e-mail: gouguohua0930@126.com).

[PDF]

Cite: Guohua Gou, "Forecast Model of Coal Demand Based on Improved Tandem Gray BP Neural Network," International Journal of Information and Education Technology vol. 7, no. 11, pp. 870-875, 2017.

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