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• W3013476570 endingPage "117444" @default.
• W3013476570 startingPage "117444" @default.
• W3013476570 abstract "Coal is not only the most important primary energy in China,but also the most stable and safe energy. Accurate prediction of coal consumption can promote the adjustment of the coal industrial structure, accelerate the coal industry to achieve high-quality development,it also provides an effective decision-making basis for the formulation of medium-and long-term coal industry development strategy. Therefore, the prediction of coal consumption has become extremely essential and urgent. In this paper,the coal consumption of Gansu in the past 20 years from 1999 to 2018 is taken as the basic data. First,the GM (1,1) forecasting model of coal consumption in Gansu was established. Based on the forecast of the coal consumption of Gansu in the past two decades,the accuracy of the model was tested. The results show that the predicted average relative error was 0.08881,and the GM(1,1) model was barely qualified with better forecasting accuracy, which is suitable for medium and long-term coal consumption forecast. Subsequently,the Markov chain prediction method was adopted to correct the GM (1,1) model,and the accuracy of the modified model was tested. The results indicate that after correcting the GM (1,1) model with Markov chain,the predicted average relative error was 0.04454,far less than before the correction,and the predicted accuracy was significantly enhanced. Finally,the Grey-Markov chain model was employed to predict the coal consumption in Gansu from 2020 to 2035,and the forecasting process was also analyzed. In addition, choose to use different scenarios to predict coal consumption, then the prediction results obtained by the two methods are compared." @default.
• W3013476570 created "2020-04-03" @default.
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• W3013476570 date "2020-05-01" @default.
• W3013476570 modified "2023-10-16" @default.
• W3013476570 title "Forecast of coal consumption in Gansu Province based on Grey-Markov chain model" @default.
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• W3013476570 doi "https://doi.org/10.1016/j.energy.2020.117444" @default.
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