Department of Economics and Management, North China Electric Power University, Baoding, China.
Department of Information Management, Peking University, Beijing, China.
Sci Total Environ. 2020 May 10;716:137117. doi: 10.1016/j.scitotenv.2020.137117. Epub 2020 Feb 5.
The accurate prediction of carbon prices poses a tremendous challenge to relevant industry practitioners and governments. This paper proposes a novel hybrid model incorporating modified ensemble empirical mode decomposition (MEEMD) and long short-term memory (LSTM) optimized by the improved whale optimization algorithm (IWOA). This model is based on the nonlinear and non-stationary characteristics of carbon price. The original carbon price is first decomposed into nine intrinsic mode functions (IMFs) and a residual using the MEEMD model. Then, the random forest method is applied to determine the input variables of each IMF and the residual, in the LSTM neural network. The carbon price is then predicted by the LSTM model optimized by the IWOA. The proposed hybrid model is applied to predict the carbon prices of Beijing, Fujian, and Shanghai to assess its effectiveness. The results reveal that the model achieved higher prediction performance than 11 other benchmark models. Our observations indicate that decomposition of carbon price can effectively improve the accuracy of prediction. Moreover, the improved LSTM model is more suitable for time series prediction. The proposed model provides a novel and effective carbon price forecasting tool for governments and enterprises.
准确预测碳价格对相关行业从业者和政府来说是一个巨大的挑战。本文提出了一种新的混合模型，该模型结合了改进的集合经验模态分解（MEEMD）和长短期记忆（LSTM），并通过改进的鲸鱼优化算法（IWOA）进行了优化。该模型基于碳价格的非线性和非平稳特征。首先，使用 MEEMD 模型将原始碳价格分解为九个固有模态函数（IMF）和一个残差。然后，随机森林方法用于确定 LSTM 神经网络中每个 IMF 和残差的输入变量。然后，通过 IWOA 优化的 LSTM 模型预测碳价格。将所提出的混合模型应用于北京、福建和上海的碳价格预测，以评估其有效性。结果表明，该模型的预测性能优于其他 11 个基准模型。我们的观察结果表明，碳价格的分解可以有效地提高预测的准确性。此外，改进后的 LSTM 模型更适合时间序列预测。该模型为政府和企业提供了一种新的、有效的碳价格预测工具。
