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.
Highlights
The coal industry is an important basic industry related to national economic and energy security.At the stage of high-quality development, the environment of coal industry development is more complex, and the problems of unbalanced, uncoordinated, and unsustainable development are still outstanding
In order to avoid falling into the local optimum when the gravitational search algorithm optimizes the parameters of the support vector machine, the improved gravitational search algorithm (IGSA) in this paper was constructed by introducing the memory function and boundary mutation strategy of PSO, shown as follows: 1. Introducing the memory function of PSO
The effectiveness of improved GSA (IGSA)–SVM in predicting coal demand was further proven by comparing the errors of effectiveness of IGSA–SVM in predicting coal demand was further proven by comparing the errors of back propagation (BP), SVM, and GSA–SVM
Summary
The coal industry is an important basic industry related to national economic and energy security. As coal is a major producer and consumer of energy, due to the lack of scientific planning, the imbalance between supply and demand has been affecting the healthy development of the coal industry and national economy for a long time in China. This paper chose the improved gravity search algorithm to optimize the parameters of the support vector machine algorithm and used the optimized algorithm to predict the values of key factors This improved intelligent algorithm model can greatly improve the prediction accuracy. In the analysis of the influencing factors of coal demand, combined with the actual situation of coal production and consumption, economic, social, and environmental constraints, this paper systematically selected 15 impact indicators from the four dimensions of economy, energy, industry, and environment.
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