Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China´s 11th Five-Year Plan (2006-1010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algorithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting. Firstly, we present the time series of GDP and load represented by load output, load imports and load exports from 1995 to 2008 as learning samples. Next, we obtain the relationships between GDP and load with SVR. Then we perform the forecasting for load output, load imports and load exports in the coming five years according to the expected growth of GDP, respectively. This paper testifies to the superiority of SVR in comparison with classical methods. Finally, the effect of forecasting results on the economic growth of China is discussed with relevant recommendations.