摘要
“双碳”政策下,火电厂碳排放量的精确预测有利于制定合理的碳减排方案。提出一种基于CEEMDAN-ISSA-BiLSTM算法的火电厂短期碳排放量预测模型。首先,利用完全集合经验模态分解(CEEMDAN)将碳排放序列分解为不同频率的子序列。然后,通过小波阈值去噪技术对高频分量进行去噪处理,并对去噪后的子序列进行信号重构。最后,为了得到预测模型最佳超参数,利用多策略改进的麻雀搜索算法(ISSA)对双向长短期记忆网络(BiLSTM)进行超参数寻优。以某火电厂200 MW机组的历史数据为基础开展验证,选取3种评价指标,与单一预测模型以及组合模型进行对比。结果表明,所提预测模型能够精准预测100天的碳排放量和变化趋势;综合各项评价指标,所构建的组合预测模型比其他模型的预测效果更好。
Under the“dual carbon”policy,the accurate prediction of carbon emissions of thermal power plants is conducive to the formulation of reasonable carbon emission reduction schemes.A short-term carbon emission prediction model for thermal power plants based on CEEMDAN-ISSA-BILSTM algorithm is presented.First,the carbon emission sequence is decomposed into sub-sequences of different frequencies using full set empirical Mode decomposition(CEEMDAN).Secondly,the high-frequency components are denoised by wavelet threshold denoising technology,and the denoised sub-sequences are reconstructed.Finally,in order to obtain the optimal hyperparameters of the prediction model,the multi-strategy modified Sparrow search algorithm(ISSA)is used to optimize the hyperparameters of bidirectional long short-term memory network(BiLSTM).Based on the historical data of a 200 MW unit of a thermal power plant,three evaluation indexes are compared with the single prediction model and the combined model.The results show that the proposed prediction model can accurately predict 100 days of carbon emissions and change trend.Combining all the evaluation indexes,the combined prediction model is better than other comparison models.
作者
桂鹏
张伟
GUI Peng;ZHANG Wei(School of Electronics and Information Engineering,Tongji University,Shanghai 201800,China;Daishan County Power Supply Company of State Grid,Zhoushan 316200,China)
出处
《电气应用》
2025年第12期71-79,共9页
Electrotechnical Application
关键词
碳排放预测
小波阈值去噪
数据分解
改进麻雀算法
双向长短期记忆网络
carbon emission prediction
wavelet threshold denoising
data decomposition
improved sparrow algorithm
bidirectional long short-term memory network
