STEAM COAL PRICE FORECASTING VIA LK-LC RIDGE REGRESSION ENSEMBLE LEARNING

Mingzhu Tang, Weiting Meng (), Qiang Hong, Huawei Wu (), Yang Wang, Guangyi Yang (), Yuehui Hu (), Beiyuan Liu (), Donglin Chen () and Fuqiang Xiong
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Mingzhu Tang: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Weiting Meng: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Qiang Hong: ��Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, P. R. China
Huawei Wu: ��Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, P. R. China
Yang Wang: ��School of Electric Engineering, Shanghai Dianji University, Shanghai 201306, P. R. China§State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, P. R. China
Guangyi Yang: �Information Center Hunan Institute of Metrology and Test, Changsha, Hunan 410014, P. R. China
Yuehui Hu: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Beiyuan Liu: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Donglin Chen: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Fuqiang Xiong: ��State Grid Hunan Extra High Voltage Substation Company, Changsha 410029, P. R. China**Substation Intelligent Operation and Inspection Laboratory, State Grid Hunan Electric Power Co., Ltd., Changsha 410029, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-19

Abstract: Steam coal is the blood of China industry. Forecasting steam coal prices accurately and reliably is of great significance to the stable development of China’s economy. For the predictive model of existing steam coal prices, it is difficult to dig the law of nonlinearity of power coal price data and with poor stability. To address the problems that steam coal price features are highly nonlinear and models lack robustness, Laplacian kernel–log hyperbolic loss–Ridge regression (LK-LC-Ridge-Ensemble) model is proposed, which uses ensemble learning model for steam coal price prediction. First, in each sliding window, two kinds of correlation coefficient are employed to identify the optimal time interval, while the optimal feature set is selected to reduce the data dimension. Second, the Laplace kernel functions are adopted for constructing kernel Ridge regression (LK-Ridge), which boosts the capacity to learn nonlinear laws; the logarithmic loss function is introduced to form the LK-LC-Ridge to enhance the robustness. Finally, the prediction results of each single regression models are utilized to build a results matrix that is input into the meta-model SVR for ensemble learning, which further develops the model performance. Empirical results from three typical steam coal price datasets indicate that the proposed ensemble strategy is reliable for the model performance enhancement. Furthermore, the proposed model outperforms all single primitive models including accuracy of prediction results and robustness of model. Grouping cross-comparison between the different models suggests that the proposed ensemble model is more accurate and robust for steam coal price forecasting.

Keywords: Steam Coal Price Forecasting; Kernel Function; Loss Function; Ridge; Ensemble Learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401412

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