中国电机工程学报2024，Vol.44Issue(16)：6551-6564,14.DOI:10.13334/j.0258-8013.pcsee.230940
基于组合时域特征提取和Stacking集成学习的燃煤锅炉NOx排放浓度预测
Prediction of NOx Emission Concentration From Coal-fired Boilers Based on Combined Time-domain Feature Extraction and Stacking Ensemble Learning
摘要
Abstract
In order to improve the prediction accuracy of NOx emission concentration at boiler outlet of thermal power plant,this paper proposes a Stacking ensemble learning model via combined time domain features.First,with the aim of mining the deep information of the data,time series analysis,CEEMDAN and statistical calculation of data standard deviation,skewness and other characteristics are used for combination,and domain feature extraction to construct the reconstructed data.Then,considering the influence of redundant variables in the reconstructed data on the accuracy of the model,the GA is used to reduce the feature dimension of the reconstructed data.Finally,in order to make the most of the advantages of each model to improve the prediction accuracy of the model,the paper constructs a Stacking integrated learning NOx emission concentration prediction model,utilizing ELM,DNN,MLP,and XGBoost as base models,and ESN as the meta-model.The experimental results show that the prediction model has a good prediction effect under different data sets,and the prediction error is less than 2％,which can accurately predict the NOx emission concentration of the boiler.关键词
NOx排放浓度/时序特征/时域特征/数据重构/Stacking集成学习Key words
NOx emission concentration/time series feature/time domain features/data reconstruction/Stacking ensemble learning分类
信息技术与安全科学引用本文复制引用
唐振浩,隋梦璇,曹生现..基于组合时域特征提取和Stacking集成学习的燃煤锅炉NOx排放浓度预测[J].中国电机工程学报,2024,44(16):6551-6564,14.基金项目
吉林省科技发展计划项目(20200401085GX).Project Supported by Jilin Science and Technology Project(20200401085GX). （20200401085GX）
