Feng Mengdan, Duan Yonghui, Wang Xiang, Zhang Jingyi, Ma Lanlan
Department of Civil Engineering, Henan University of Technology, No. 100, Lianhua Street, Gaoxin District, Zhengzhou, 450001, China.
Department of Civil Engineering, Zhengzhou University of Aeronautics, No. 15, Wenyuan West Road, Zhengdong New District, Zhengzhou, 450015, China.
Sci Rep. 2023 Oct 27;13(1):18447. doi: 10.1038/s41598-023-45524-2.
It is essential to predict carbon prices precisely in order to reduce CO emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO-XGBOOST-CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO-XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO-XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO-XGBOOST-CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model's validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices.
为了减少二氧化碳排放并缓解全球变暖，精确预测碳价格至关重要。针对单一机器学习模型在碳价格预测问题上预测能力不足的局限性，本文提出了一种基于灰狼优化器（GWO）、极端梯度提升（XGBOOST）和自适应噪声完备总体经验模态分解（CEEMDAN）相结合的碳价格预测模型（GWO-XGBOOST-CEEMDAN）。首先，采用随机森林（RF）方法筛选主要碳价格指标并确定主要影响因素。其次，建立GWO-XGBOOST模型，并利用GWO算法优化XGBOOST模型参数。最后，使用CEEMDAN方法对GWO-XGBOOST模型的残差序列进行分解和校正，得到GWO-XGBOOST-CEEMDAN模型。对广东、湖北和福建三个碳排放交易市场进行了实验预测，以验证该模型的有效性。基于实验结果表明，所提出的混合模型与对比模型相比具有更高的预测精度，为未来碳价格的预测提供了一种有效的实验方法。
