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Open AccessArticle

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

School of Economics and Management, North China Electric Power University, Beijing 102206, China
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Author to whom correspondence should be addressed.
Energies 2019, 12(12), 2249; https://doi.org/10.3390/en12122249
Received: 1 April 2019 / Revised: 1 June 2019 / Accepted: 10 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Sustainability of Fossil Fuels)
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. View Full-Text
Keywords: coal consumption forecasting; support vector machine; improved gravitational search algorithm; grey relational analysis coal consumption forecasting; support vector machine; improved gravitational search algorithm; grey relational analysis
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Li, Y.; Li, Z. Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm. Energies 2019, 12, 2249.

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