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Abstract
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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
Submitted: September 27, 2012
Accepted: December 11, 2012
Published: May 10, 2013 |
Abstract:
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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.
Key words: BP neural network, grey forecast, NOx emissions forecast, tandem gray BP neural network, , ,
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Cite this Reference:
Jianguo Zhou, Lin Meng and Xiaodan Pan, . Thermal Power Industry NOX Emissions Forecast Based on Improved Tandem Gray BP Neural Network. Research Journal of Applied Sciences, Engineering and Technology, (19): 4716-4721.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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