School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China.
Environ Sci Pollut Res Int. 2024 Mar;31(14):20898-20924. doi: 10.1007/s11356-024-32333-x. Epub 2024 Feb 21.
As the global greenhouse effect intensifies, carbon emissions are gradually becoming a hot topic of discussion. Accurate carbon emissions prediction is an important foundation to realize carbon neutrality and peak carbon dioxide emissions. To accurately predict carbon emissions, a multi-factor combination prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory optimized by lemurs optimizer (LOBiLSTM) and least squares support vector machine optimized by lemurs optimizer (LOLSSVM), named ICEEMDAN-LOBiLSTM-LOLSSVM, is proposed. Firstly, the influencing factors of carbon emissions are selected by Spearman correlation coefficient, and carbon emissions are decomposed into intrinsic mode functions (IMFs) by ICEEMDAN. Secondly, the influencing factors and IMFs are input into LOBiLSTM and LOLSSVM respectively for prediction. Then, the point prediction results are obtained by weighting the prediction results of LOBiLSTM and LOLSSVM. Finally, probability density function of point prediction error is calculated by kernel density estimation, and the interval prediction results are calculated according to different confidence intervals. Carbon emissions of China and Germany are selected to verify the superiority of ICEEMDAN-LOBiLSTM-LOLSSVM. The experiment shows that RMSE, MAE, MAPE, and R of the proposed model are 0.4468, 0.3612, 0.0120, and 0.9839 respectively for China, which is the best among the nine models, as well as for Germany.
随着全球温室效应的加剧，碳排放逐渐成为讨论的热点。准确预测碳排放是实现碳中和和二氧化碳排放峰值的重要基础。为了准确预测碳排放，提出了一种基于改进的完全集成经验模态分解自适应噪声（ICEEMDAN）、优化的狐猴优化器的双向长短时记忆网络（LOBiLSTM）和优化的狐猴优化器的最小二乘支持向量机（LOLSSVM）的多因素组合预测模型，命名为 ICEEMDAN-LOBiLSTM-LOLSSVM。首先，通过 Spearman 相关系数选择碳排放的影响因素，然后通过 ICEEMDAN 将碳排放分解为固有模态函数（IMF）。其次，将影响因素和 IMF 分别输入到 LOBiLSTM 和 LOLSSVM 中进行预测。然后，通过对 LOBiLSTM 和 LOLSSVM 的预测结果进行加权，得到点预测结果。最后，通过核密度估计计算点预测误差的概率密度函数，并根据不同的置信区间计算区间预测结果。选择中国和德国的碳排放数据来验证 ICEEMDAN-LOBiLSTM-LOLSSVM 的优越性。实验结果表明，对于中国和德国，所提出模型的 RMSE、MAE、MAPE 和 R 分别为 0.4468、0.3612、0.0120 和 0.9839，在 9 种模型中表现最好。
