Carbon price forecasting based on data decomposition, adaptive boosting algorithm, and Elman neural network

Shi Yin; Hui Liu; Yanfei Li

doi:10.1117/12.2673731

22 May 2023 Carbon price forecasting based on data decomposition, adaptive boosting algorithm, and Elman neural network

Shi Yin, Hui Liu, Yanfei Li

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401S (2023) https://doi.org/10.1117/12.2673731
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

The prediction of carbon price can contribute to the reduction of carbon emissions. A carbon price forecasting model based on the data decomposition method, adaptive boosting (AdaBoost) algorithm, and Elman neural network (ENN) is proposed in this paper, which firstly decomposes original data into subsequences by the variational mode decomposition algorithm, and then combines the ENN models by the AdaBoost.RT algorithm to forecast each subsequence, and finally the predicted results of each subsequence are combined into the final prediction results. Using the carbon price in Beijing, China as the experimental data, the evaluation errors certify that the proposed ensemble model can achieve a better forecasting effect than the single models including the ENN, extreme learning machine, and long short-term memory network and the ensemble model AdaBoost.RT-ENN, which proves the effectiveness of the data decomposition and adaptive boosting algorithm.

Citation Download Citation

Shi Yin, Hui Liu, and Yanfei Li "Carbon price forecasting based on data decomposition, adaptive boosting algorithm, and Elman neural network", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401S (22 May 2023); https://doi.org/10.1117/12.2673731

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