The Analysis Of Power Demand In China Using Cointegration Model And Combination Forecasting Method

Today our country is in a new period of comprehensive building the affluent society. Along with the rapid development of our economy, energy consumption is also growing fast. How to programming the energy distribution reasonably, and use of limited resources to accelerate economic development, has become an increasingly important issue. A growing number of scholars involved in the research of inherent links between energy consumption and economic growth, is also developing a number of methods for forecasting energy consumption. Correct and reasonable to forecast energy consumption not only can guide the allocation of resources, but also provide valuable information for the formulation of economic policies. This paper discuss the causation of electricity consumption, GDP, people, price and structure variables, using the pop method co-integration and error correction model, and integrate the intervention analysis method which can correct the forecast bias. In addition, we also test the data generated process of our electricity consumption series, build an auto regression model with the dummy variables, from another point of the electricity consumption forecast. Because of the complexity of the energy system, it is difficult to solve the problems comprehensively and accurately with only one single forecasting method, the advantage of the various methods can be integrated by the combination forecasting method, which can make the forecasting results more precise and stable. Four forecasting methods (co-integration and error correction model, auto-regression, exponential smoothing, grey forecasting model) in this study are discussed, the combination-forecasting model is found for evaluation of the electricity consumption in China, and improve the inadequate of single method, then make our forecasting result extrapolated to 2025.