锅炉技术2026，Vol.57Issue(2)：11-17,7.
基于TCN-Transformer模型的低温省煤器入口烟气参数预测
Prediction of Inlet Flue Gas Parameters of Low Temperature Economizer Based on TCN-Transformer Model
摘要
Abstract
Taking the low-temperature economizer of a 1 055 MW unit in a thermal power plant as an example,a low-temperature economizer inlet flue gas parameter prediction model that integrates the Temporal Convolutional Network(TCN)and the Transformer architec-ture is proposed.The multivariate characteristic time series is modeled by fully considering the operating characteristics of the unit,and the prediction of the flue gas temperature and flow rate at the inlet of the low-temperature economizer is realized.Through comparative analysis with the traditional Back Propagation(BP)neural network,Long Short-Term Mem-ory(LSTM)model and Least Squares Support Vector Machine(LSSVM)model,the results show that the constructed TCN-Transformer model has higher accuracy and generalization ability in flue gas parameter prediction,and the relative errors of the prediction of the flue gas temperature and flow rate at the inlet of the low-temperature economizer are 0.22%and 1.67%,respectively.关键词
低温省煤器/烟气温度/TCN-Transformer/时序预测Key words
low-temperature economizer/flue gas temperature/TCN-Transformer/time series prediction分类
能源科技引用本文复制引用
姜正雄,韦栋梁,周昊..基于TCN-Transformer模型的低温省煤器入口烟气参数预测[J].锅炉技术,2026,57(2):11-17,7.基金项目
中央高校基础研究基金(2022ZFJH04) （2022ZFJH04）
