| 引用本文: | 仵晓聪a,冯 鑫a,蒋 豪b.基于多头注意力 CNN-LSTM 碳排放量预测研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（3）：116-124 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 由于碳排放具有不规律性和非线性的特点,针对如何提升碳排放量预测效果,提出一种基于多头注意
力 CNN-LSTM 的碳排放预测方法。 方法 基于 CNN 网络具有的空间特征提取优势和 LSTM 算法有效利用长、短期
时间的依赖关系,引入多头注意力机制以分配不同权重的方式,采用多头注意力机制引导下的 CNN-LSTM 模型对
我国碳排放量进行预测。 结果 对过去 1 724 d 碳排放量的数据集进行训练和预测,发现测试集的 RMSE、MASE、
MAPE 指标数值分别为 0. 080 2、 0. 302 0、0. 105 7;与 BP 神经网络算法、ARIMA(2,1,4)、GM(1,1)进行对比,可以
明显看出设计的模型在 3 种评价指标方面均优于其余预测算法。 结论 采用基于多头注意力的 CNN-LSTM 碳排放
预测方法能够有效提高预测精确度,适用于时间序列预测。 |
| 关键词: 碳排放量预测 多头注意力机制 CNN-LSTM 模型 多头注意力 CNN-LSTM 预测模型 |
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| Research on Carbon Emission Prediction Based on Multi-Head Attention CNN-LSTM |
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WU Xiaoconga FENG Xina JIANG Haob
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a. Chongqing Key Laboratory of Intelligent Equipment Green Design and Manufacturing b. School of Mathematics and
Statistics Chongqing Technology and Business University Chongqing 400067 China
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| Abstract: |
| Objective Due to the irregularity and nonlinearity of carbon emissions this study proposes a carbon emission
prediction method based on multi-head attention CNN-LSTM to enhance the accuracy of carbon emission predictions.
Methods Leveraging the spatial feature extraction capabilities of CNN networks and the effective utilization of long-term
and short-term dependencies by the LSTM algorithm a multi-head attention mechanism was introduced to allocate different
weights. A CNN-LSTM model guided by the multi-head attention mechanism was employed to predict carbon emissions in
China. Results The research selected a dataset of China?? s carbon emissions over the past 1 724 days and used the model
proposed in the paper to conduct training and prediction on this dataset. After testing the values of the RMSE MASE
and MAPE of the model on the test set were 0. 080 2 0. 302 0 and 0. 105 7 respectively. Comparing with the BP neural
network algorithm ARIMA 2 1 4 and GM 1 1 the model designed in the paper outperformed the other
prediction algorithms in terms of these three evaluation indicators. Conclusion The carbon emission prediction method
based on multi-head attention CNN-LSTM can effectively improve the prediction accuracy and is suitable for time series
prediction. |
| Key words: carbon emission prediction multi-head attention mechanism CNN-LSTM model multi-head attention CNNLSTM prediction model |
