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Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm

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  • Yanbin Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Zhen Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

The main target of the energy revolution in the new period is coal, but the proportion of coal in primary energy consumption will gradually decrease. As coal is a major producer and consumer of energy, analyzing the trend of coal demand in the future is of great significance for formulating the policy of coal development planning and driving the revolution of energy sources in China. In order to predict coal demand scientifically and accurately, firstly, the index system of influencing factors of coal demand was constructed, and the grey relational analysis method was used to select key indicators as input variables of the model. Then, the kernel function of SVM (support vector machine) was optimized by taking advantage of the fast convergence speed of GSA (gravitational search algorithm), and the memory function and boundary mutation strategy of PSO (particle swarm optimization) were introduced to improve the gravitational search algorithm, and the improved GSA (IGSA)–SVM prediction model was obtained. After that, the effectiveness of IGSA–SVM in predicting coal demand was further proven through empirical and comparative analysis. Finally, IGSA–SVM was used to forecast China’s coal demand in 2018–2025. According to the forecasting results, relevant suggestions about coal supply, consumption, and transformation are put forward, providing scientific basis for formulating an energy development strategy.

Suggested Citation

  • Yanbin Li & Zhen Li, 2019. "Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm," Energies, MDPI, vol. 12(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2249-:d:239277
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    Cited by:

    1. Mengshu, Shi & Yuansheng, Huang & Xiaofeng, Xu & Dunnan, Liu, 2021. "China's coal consumption forecasting using adaptive differential evolution algorithm and support vector machine," Resources Policy, Elsevier, vol. 74(C).
    2. Chengzhou Li & Ningling Wang & Hongyuan Zhang & Qingxin Liu & Youguo Chai & Xiaohu Shen & Zhiping Yang & Yongping Yang, 2019. "Environmental Impact Evaluation of Distributed Renewable Energy System Based on Life Cycle Assessment and Fuzzy Rough Sets," Energies, MDPI, vol. 12(21), pages 1-17, November.
    3. Ivan Lorencin & Nikola Anđelić & Vedran Mrzljak & Zlatan Car, 2019. "Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation," Energies, MDPI, vol. 12(22), pages 1-26, November.
    4. Yujing Liu & Ruoyun Du & Dongxiao Niu, 2022. "Forecast of Coal Demand in Shanxi Province Based on GA—LSSVM under Multiple Scenarios," Energies, MDPI, vol. 15(17), pages 1-16, September.
    5. Wang, Delu & Tian, Cuicui & Mao, Jinqi & Chen, Fan, 2023. "Forecasting coal demand in key coal consuming industries based on the data-characteristic-driven decomposition ensemble model," Energy, Elsevier, vol. 282(C).

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