English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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