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2013 (Vol. 5, Issue: 19)
Article Information:

Thermal Power Industry NOX Emissions Forecast Based on Improved Tandem Gray BP Neural Network

Jianguo Zhou, Lin Meng and Xiaodan Pan
Corresponding Author:  Jianguo Zhou 

Key words:  BP neural network, grey forecast, NOx emissions forecast, tandem gray BP neural network, , ,
Vol. 5 , (19): 4716-4721
Submitted Accepted Published
September 27, 2012 December 11, 2012 May 10, 2013
Abstract:

In this study, we build a new thermal power sector NOx emissions prediction model of tandem gray BP neural network. Firstly we use 1994-2010 years NOx emissions 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 NOx emissions factors as the BP neural network input, 1994-2010 year of NOx emissions data for training and testing. Lastly we proceed to predict thermal power industry NOx emissions in China in 2013 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.
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  Cite this Reference:
Jianguo Zhou, Lin Meng and Xiaodan Pan, 2013. Thermal Power Industry NOX Emissions Forecast Based on Improved Tandem Gray BP Neural Network.  Research Journal of Applied Sciences, Engineering and Technology, 5(19): 4716-4721.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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