Abstract: Energy is the material basis for the survival and development of human society and it plays an important strategic role in national economy.With the development of society, it shows an increasing demand of energy, so effective management is important for energy use. Energy consumption prediction is the theory premise of energy supply and demand management, therefore, establishing a reliable energy consumption forceasting model is particularly significant. At present, the existing models mainly includes two types:single prediction models and hybrid prediction models.In this study' a group method of data handling (GMDH) based hybrid forecasting model (GHFM) is proposed for China's energy consumption prediction. In this model, GMDH based auto-regression is first constructed in the original energy consumption time series, the linear trend of time series is predicted, and residual series (i.e., non-linear sub-series) is obtained. Considering the complexity of non-linear sub-series prediction, BP neural network, support vector regression, genetic programming and RBF neural network are constructed respectively, and then combined forecasting model is condueted selectively by GMDH in non-linear sub-series and the combination prediction are obtained for the non-linear sub-series. At last, the total energy consumption forecast values are integrated from the two parts above. Empirical analysis is conducted on the total energy consumption and oil consumption time series from China Statistical Yearbook of energy statistics data (2014) and the results show that the forecast performance of GHFM model is better than other models. In addition, the out-of-sample forecasts of China's total energy consumption from 2015 to 2020 is given based on GHFM model. The proposed model can be used to improve the accuracy of energy consumption forecasting. The model can also be applied to other time series forecasting problems, including container throughput forecasting, crude oil price forecasting, electricity load forecasting, stock price forecasting, et al.

Key words: energy consumption, GMDH, hybrid forecasting, combined forecasting