Prediction of NOx Emissions of Coal-fired Power Plants Based on Mutual Information-graph Convolutional Neural Network

被引：0

作者：

Liu H. ^{[1
]}

Wang Y. ^{[1
]}

Li X. ^{[1
]}

Yang G. ^{[1
]}

机构：

[1] School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 03期

关键词：

Characteristic adjacency matrix; Graph convolutional neural network; Mutual information; NO[!sub]x[!/sub] emissions;

D O I：

10.13334/j.0258-8013.pcsee.202540

中图分类号：

学科分类号：

摘要：

NOx emission prediction model of coal-fired power plant can improve denitrification economy. The NOx emission mechanism is complex, and there are many variablesmany variables that effect the NOx emissions. The effective fusion of the information between the correlation variables can improve the NOx emission prediction accuracy. This paper presents presented a NOx emission prediction model through mutual information- graph convolution neural network (MI-GCN). Based on the operation parameters of the 660MW coal-fired power plant, the mutual information between characteristic variables affecting NOx emission is was calculated, the adjacency relationship between characteristic variables is was designed, the characteristic adjacency matrix is was obtained, and the NOx emission prediction model based on graph convolution neural network is was constructed. The proposed NOx prediction model is was compared with the typical NOx prediction models based on long short time memory (LSTM), BPNN and least squares support vector machine (LS-SVM). The experimental results show that the MI-GCN prediction model has better generalization ability and higher prediction accuracy. © 2022 Chin. Soc. for Elec. Eng.

引用

页码：1052 / 1059

页数：7

共 20 条

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