Forecast And Early-Warning On Coal Supply And Demand Balance In China

"Overplus—abating—again overplus"is the odd circle that our coal industry has been in. The basic reason is the incorrect prediction of total supply and demand,lack of a monitoring and early warning system. For coal forecast and early-warning system, there is little study about it in our country, in developed countries, the studies are not in open due to involving economy safety, national security and foreign strategy, therefore a useful model that can be used for reference for the study of coal early-alarming and prediction is not existed. This paper done some pioneering work about study on coal prediction and early-alarming.Based on statistic software SPSS and Eviews, by the method of combining theoretical analysis and substantial evidence analysis,the paper analysed the factors that influencing coal demand and supply in detail, then selected the indicators initially, set up a foundation for the system of coal early-alarming and prediction.Based on the theory of analysis on the time difference and the causation theory, the paper set up a target system of coal demand and supply respectively from the factors that influencing coal demand and supply by using software Eviews.To improve forecast precision, the paper established different forewarning models according to index sample size. Using the BP neural networks theory, ARMA theory and stepwise regression theory, the paper set up a combination forecasting model for short-term prediction of coal demand, in which including BP neural networks model, ARMA model and stepwise regression model, their forecasting error square sum were 171162159, 496420810, 125467320 and 85886631 from the year 1986 to 2006, from which we get the result that the forecasting result of combination model is much better than the local estimation precision for each single model. During the years 2008-2010, the forecasting of coal demands were 26.9, 27.5 and 28.6 million tons by using combination forecasting model.Unit root test shows that coal supply, GDP, consumption, railway transportation and coal consumption are all nonstationary variables, but the result of the Johansen Cointegration Test demonstrates cointegrated relationship among them, and there are two cointegration vectors, unbalanced information errors will lost if designing a VAR model with difference method, so a VEC model was established to predict the coal supply in this paper. Maximum errors of actual and forecasting value of VEC is 4.95%, minimum error is 0.08%. During the years 2008-2010, the forecasting results are 26.9, 27.5 and 28.6 million tons by using VEC model.The paper established SD model for coal demand and supply medium-term and long-term forecast, in which including five interaction subsystems of coal supply, coal consumption, railway transportation GDP and coal investment. Maximum errors of actual and forecasting value of coal consumption is 4.92%, minimum error is 0.67%, maximum errors of actual and forecasting value of coal demand is 9.54%, minimum error is 1.43%.In 2015 and 2020 years, the forecasting results of coal consumption and coal demand are 30.1,29.1和35.4,33.7 million tons. Set up different situations for simulating calculation of coal demand and supply system.Based on cyclic fluctuation theory, the paper studied fluctuation law of coal demand and supply, measured and tested its fluctuation cycle by using tendency separating method; the examination result showed that the coal demand and supply are periodic wave. In order to regard demand and supply as a system, not two independent variables, the paper put forward a conception, that is the system demand-supply velocity. By using the method of the combination of economic mechanics and catastrophe theory, the paper calculated critical values of growth rate of coal demand and supply and coal demand-supply velocity alert limits, eventually designed the comprehensive warning limit value and warning degree interval, designed early-warning programme with VB.NET, which is used to provides early warning of coal demand and supply from 2008 to 2020.