热力发电2026，Vol.55Issue(8)：33-44,12.DOI:10.19666/j.rlfd.202602032
基于CNN-BiLSTM-Attention的燃煤电厂二氧化碳排放预测模型
Carbon dioxide emission prediction model for coal-fired power plants based on CNN-BiLSTM-Attention
摘要
Abstract
[Objective]Coal-fired power plants play a pivotal role in China's power system,and accurate forecasting of their CO2 emissions is crucial for achieving energy-saving and emission-reduction targets.To address this challenge,this paper proposes a short-term CO2 emission prediction model for coal-fired power plants based on CNN-BiLSTM-Attention.[Methods]First,feature selection is performed by analyzing the correlation between CO2 emission rates and various input variables,including unit load,SO2 emission rate,NOx emission rate,and environmental factors,thereby reducing redundant information that may impair model training.Specifically,the Pearson correlation is used to quantify the linear relationship between each candidate feature and the CO2 emission rate,and features with a correlation coefficient below 0.2 are excluded.The raw operational data are then normalized to the range[0,1]and segmented into samples using a sliding time window to capture temporal dynamics.Subsequently,the selected features are fed into the proposed CNN-BiLSTM-Attention model.In this hybrid architecture,the CNN layer extracts local patterns from the input sequence,the BiLSTM layer captures both forward and backward temporal dependencies,and the attention mechanism assigns adaptive weights to the most informative time steps.To further enhance performance,an improved Kepler optimization algorithm(IKOA)is introduced to optimize the model's hyperparameters.The model is validated using real-world operational data from coal-fired power plant units.In addition to root mean square error(RMSE),the mean absolute error(MAE)and the coefficient of determination(R2)are also computed to comprehensively evaluate prediction performance.Comparative experiments show that among LSTM,BiLSTM,CNN-BiLSTM,and CNN-BiLSTM-Attention,the last model achieves the lowest RMSE of 24.329 2 t/h,compared to 26.261 0 t/h,26.039 3 t/h,and 25.429 3 t/h for the other three models,respectively.Moreover,when optimizing the CNN-BiLSTM-Attention model with four metaheuristic algorithms(genetic algorithm(GA),whale optimization algorithm(WOA),Kepler optimization algorithm(KOA),and IKOA),the IKOA yields the best fitness value of 21.584 1 t/h,slightly outperforming WOA(21.594 6 t/h),KOA(22.136 0 t/h),and GA(22.807 2 t/h).Results demonstrate that the proposed model exhibits strong transferability across units of similar capacity and effectively captures CO2 emission trends over time,although prediction errors increase during seasonal transitions due to variations in operating conditions and coal quality.[Results]The proposed model consistently outperforms baseline models in both prediction accuracy and stability.[Conclusion]The CNN-BiLSTM-Attention model enables effective CO2 emission forecasting for coal-fired power plants even without direct CO2 measurements,showing significant applicability and potential for broader deployment.This capability is particularly valuable for plants lacking continuous emission monitoring systems(CEMS),offering a cost-effective alternative for CO2 estimation.However,long-term or cross-seasonal predictions require further consideration of external factors such as coal quality.This study is primarily validated on limited units from a single plant;thus,its generalizability across different regions,coal sources,and operational strategies warrants further investigation.Future work will incorporate direct coal-quality indicators through multi-source data fusion or online updating mechanisms to enhance long-term predictive performance and robustness.关键词
CO2排放预测/燃煤电厂/CNN/BiLSTM/注意力机制/KOAKey words
CO2 emission forecasting/coal-fired power plant/CNN/BiLSTM/attention mechanism/KOA引用本文复制引用
何海飞,周良松,陈骏鹏,赵子行,陈二强,支长双,苗世洪..基于CNN-BiLSTM-Attention的燃煤电厂二氧化碳排放预测模型[J].热力发电,2026,55(8):33-44,12.基金项目
国网河南省电力公司科技项目(52170225000B) Science and Technology Project of State Grid Henan Electric Power Company(52170225000B) （52170225000B）
