中国电力2026，Vol.59Issue(6)：60-75,16.DOI:10.11930/j.issn.1004-9649.202601038
考虑时序波动信息挖掘的双重注意力TCN-BiGRU短期电-碳价格耦合预测方法
A dual-attention TCN-BiGRU short-term electricity-carbon price coupling prediction method incorporating time-series fluctuation information mining
摘要
Abstract
Accurate forecasting of short-term spot electricity and carbon prices is crucial for decision-making in electricity market trading,carbon market operation,and their coordinated management.However,electricity and carbon price series are influenced by multiple complex factors,including energy structure,policy regulation,and renewable energy fluctuations,exhibiting high volatility and nonlinearity,which poses significant challenges to forecasting accuracy.Therefore,this paper proposes a dual-attention temporal convolutional net-work,bidirectional gated recurrent network(DA-TCN-BiGRU)short-term electricity-carbon price coupling forecasting method considering time-series fluctuation information mining.First,the central collision optimization-based variational mode decomposition algorithm is used to decompose the electricity and carbon price series into multi-frequency subsequences,so as to fully extract their fluctuation modes at different time scales.Second,the correlation strength between each feature in the high-dimensional feature set and electricity-carbon prices is evaluated based on the maximal information coefficient,and key features are selected.On this basis,a dual-attention TCN-BiGRU deep learning model is constructed to forecast carbon prices,and the predicted carbon price values are further input into the same framework as key exogenous variables to predict electricity prices,achieving progressive coupling forecasting of electricity-carbon prices.Finally,the case study based on actual data from the Hubei Province electricity-carbon market shows that the proposed method has higher accuracy and stronger stability in electricity-carbon price prediction,verifying the effectiveness and superiority of the model.关键词
电-碳价格/优化变分模态分解/最大互信息系数/双重注意力机制/时间卷积网络/双向门控循环网络Key words
electricity-carbon price/optimized variational mode decomposition/maximal information coefficient/dual attention mechanism/temporal convolutional network/bidirectional gated recurrent network引用本文复制引用
刘思宇,张成,江涛,肖雅,易雅雯,张玉欣,陈新宇..考虑时序波动信息挖掘的双重注意力TCN-BiGRU短期电-碳价格耦合预测方法[J].中国电力,2026,59(6):60-75,16.基金项目
国家自然科学基金资助项目(72293601,72488101) （72293601,72488101）
国家杰出青年科学基金资助项目(72325006). This work is supported by National Natural Science Foundation of China(No.72293601,No.72488101),and National Science Fund for Distinguished Young Scholars of China(No.72325006). （72325006）
