Economics and Management Department, North China Electric Power University, Baoding, 071000, Hebei, China.
Environ Sci Pollut Res Int. 2021 Oct;28(40):56580-56594. doi: 10.1007/s11356-021-14591-1. Epub 2021 Jun 1.
The recovery of carbon emissions in the past 2 years has alerted us that carbon emissions are a long-term process, and setting short-term emission reduction targets can more effectively curb the rising trend of carbon emissions. Therefore, the research on short-term prediction of carbon emissions is particularly important. In this paper, the idea of "decomposition-prediction" is put forward in the short-term prediction of carbon emissions, and the combined model of "decomposition-prediction" is constructed. The model is composed of ensemble empirical mode decomposition (EEMD) and the backpropagation neural network based on particle swarm optimization (PSOBP). It is also the first time that EEMD has been applied to the field of carbon emission prediction. Firstly, EEMD is used to decompose the daily carbon emission monitoring data into 6 modal functions and one residual sequence, and the partial autocorrelation function (PACF) is used to determine the input of each modal function. Then, PSOBP was used to predict. Finally, adding the prediction results of each sequence to get the final prediction results. To verify the effectiveness and superiority of the EEMD-PSOBP model, 14 comparative models were constructed, and the prediction effect of the models was evaluated by R, RMSE, and MAPE. All the prediction results show that the proposed model has the best prediction performance (R=0.9507, RMSE=0.3431, MAPE=0.093). Compared with PSOBP, the R of EEMD-PSOBP was increased by 63.58%, and RMSE and MAPE were decreased by 65.18% and 64.23%, respectively. The accuracy of prediction can be improved significantly by decomposing before predicting. It was also found that EEMD had the highest predictive performance improvement. Therefore, this model will have broad development prospects in the field of short-term carbon emission prediction in the future.
在过去的 2 年中，碳排放量的回升引起了我们的警觉，即碳排放量是一个长期的过程，设定短期减排目标可以更有效地遏制碳排放量的上升趋势。因此，对碳排放量的短期预测研究尤为重要。本文在碳排放量的短期预测中提出了“分解-预测”的思想，并构建了“分解-预测”的组合模型。该模型由集合经验模态分解（EEMD）和基于粒子群优化的反向传播神经网络（PSOBP）组成，这也是 EEMD 首次应用于碳排放量预测领域。首先，利用 EEMD 将日碳排放量监测数据分解为 6 个模态函数和一个残差序列，利用偏自相关函数（PACF）确定各模态函数的输入。然后，采用 PSOBP 进行预测。最后，将各序列的预测结果相加得到最终的预测结果。为了验证 EEMD-PSOBP 模型的有效性和优越性，构建了 14 个对比模型，通过 R、RMSE 和 MAPE 对模型的预测效果进行评价。所有预测结果均表明，所提出的模型具有最佳的预测性能（R=0.9507、RMSE=0.3431、MAPE=0.093）。与 PSOBP 相比，EEMD-PSOBP 的 R 值提高了 63.58%，RMSE 和 MAPE 分别降低了 65.18%和 64.23%。预测前分解可以显著提高预测精度。还发现 EEMD 的预测性能提升最高。因此，该模型在未来的短期碳排放量预测领域将具有广阔的发展前景。
