Abstract
In order to improve the short-term prediction accuracy of carbon emissions, a new hybrid daily carbon emission prediction model is proposed in this paper, and secondary decomposition is introduced for carbon emission prediction for the first time. First, the data is decomposed into several IMFs by complementary ensemble empirical mode decomposition (CEEMD). Then, the IMF1 is decomposed again by wavelet decomposition (WD), and the rest IMFs are reconstructed according to the sample entropy (SE). Finally, Long Shor Term Memory (LSTM) is used to predict daily carbon emission. In order to verify the validity of the model, the daily carbon emission data of China, United States (US) and World are used for empirical analysis. In the performance comparison experiment, CEEMD-WD-LSTM model proposed in this paper has the best performance among all comparison models, and secondary decomposition of carbon emissions significantly improves the MAPE, R2 and RMSE. The results show that the model proposed in this paper is effective and robust, and can predict daily carbon emissions more accurately.
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Acknowledgement
This paper is supported by Key Project of National Social Science Foundation of China “Research on Platform Enterprise Governance” (Grant No. 21AZD118), the Fundamental Research Funds for the Central Universities (Project No. 2021MS105) and Social Science Fund project of Hebei Province (HB20GL027).
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Zhang, X., Zhang, W. (2022). A Hybrid Daily Carbon Emission Prediction Model Combining CEEMD, WD and LSTM. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_46
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