School of Mathematics, China University of Mining and Technology
China Coal Transportation and Distribution Association
Beijing Coal Times Technological Development Co.
Jiangsu Center for Applied Mathematics, China University of Mining and Technology

煤炭作为全球能源结构的关键支柱,其价格波动对经济、能源市场、环境政策及工业生产成本具有广泛影响,因而准确预测煤炭价格对于维护能源市场的稳定性、有效控制成本以及管理风险具有至关重要的作用。提出一个深度学习模型(XGBGCN)来解决煤炭价格预测问题。该模型融合XGBoost算法,用于分析影响煤炭价格的关键特征,同时结合图卷积网络 (GCN) 模型,利用这些关键特征进行煤炭价格的预测。XGBoost模型能够有效地从大量与煤炭价格相关的因素中提取出关键特征,从而降低模型的复杂性,并提高预测精度。具体而言,XGBGCN模型首先利用XGBoost算法来寻找与煤炭价格相关性大的特征,该特征包括用电量、其他地区煤炭价格等。利用选择的特征构建煤炭价格关联图,并结合特征矩阵,作为GCN模型的输入,进行煤炭价格预测。此外,在真实煤炭价格及其影响因素数据集上对煤炭价格进行预测,结果表明,与一些现有模型相比,所提出的XGBGCN模型,能够较为准确的预测煤炭价格。

Coal, as a key pillar of the global energy structure, has widespread effects on the economy, energy markets, environmental policies, and industrial production costs due to its price fluctuations. Accurately predicting coal prices is crucial for maintaining energy market stability, effectively controlling costs, and managing risks. This paper introduces a deep learning model—Extreme Gradient Boosting-Graph Convolutional Network (XGBGCN)—to address the problem of coal price prediction. The model integrates the Extreme Gradient Boosting (XGBoost) algorithm to analyze critical features affecting coal prices and the Graph Convolutional Network (GCN) model to predict prices based on these features. The XGBoost model can effectively extract key features from a large number of factors related to coal prices, thereby reducing model complexity and improving prediction accuracy. Specifically, the XGBGCN model first uses the XGBoost algorithm to identify features highly correlated with coal prices, such as electricity consumption, coal prices in other regions, etc. It then constructs an adjacency matrix of the coal price correlation graph using the selected features, combined with the feature matrix, as input to the GCN model for coal price prediction. Additionally, this paper predicts coal prices on an actual coal price and its influencing factors dataset, and the results show that the proposed XGBGCN model can accurately predict coal prices and outperform several existing models.

国家自然科学基金面上项目(72471227,72071202)

邵枫, 冯雨, 鲁义广, 耿国强, 邵虎. 融合XGBoost与图卷积网络的煤炭价格预测研究. 煤炭经济研究. 2025, 45(2): 39-46

SHAO Feng, FENG Yu, LU Yiguang, GENG Guoqiang, SHAO Hu. Research on coal price prediction integrating XGBoost and graph convolutional network. Coal Economic Research. 2025, 45(2): 39-46