Short-term prediction of carbon emissions based on the EEMD-PSOBP model.

Abstract

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 R2, RMSE, and MAPE. All the prediction results show that the proposed model has the best prediction performance (R2=0.9507, RMSE=0.3431, MAPE=0.093). Compared with PSOBP, the R2 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.