| Energy is an indispensable material basis of human survival, economic development, social progress and modern civilization,With the development of social economy, energy demand is also growing.Therefore, the demand for energy research has important theoretical and practical significance. Energy demand forecast is from a country, region or specific energy consumption's past and present . According to their consumption behavior, summarized the various factors affecting energy consumption, for consumption and the relationship between these factors. Therefore, the demand for energy modeling and forecasting is one of the foundations to develop energy development strategy, planning the deployment.In recent years, many scholars has been studied the energy demand forecast, there are many prediction methods. The article take the economic growth, industrial structure, energy consumption, population and urbanization, the level of consumption, technological progress and environmental policies as energy demand factors. Use the total energy demand time-series data from 1978 to 2008, through the time series, gray theory, and BP neural network respectively, forecasted the total energy demand of China from 2010 to 2015.Combination forecasting model has higher prediction accuracy than a single prediction model ,it can enhance the stability of prediction, and the higher forecast to adapt to future environmental change, In this paper, the minimum sum of squares prediction error as the objective function of the linear combination forecast model Calculated the weight factor of the time series, gray theory, and BP Neural Network Model, Using data from 1978 to 2008 to build and test model. The results show that the the average relative error of combined model forecasted is 2.19%,which is 1.93% smaller than time series, 2.14% smaller than gray theory and 1.12% smaller than BP neural network. The combined model forecasted that by 2015 China's total energy demand will reach 3,857,810,000 tons of standard coal. |
PDF Full Text Request