It is of great significance to develop accurate forecasting models for China's energy consumption. The energy consumption time series often have the characteristics of complexity and nonlinearity, and the single model cannot achieve satisfactory forecasting results. Therefore, in recent years, more and more scholars have tried to build up hybrid model to handle this issue, in which the divide and rule method is the most popular one. However, the existing divide and rule models often predict the energy consumption subseries after decomposing with the single forecasting model. This study introduces the group method of data handling technique for energy consumption forecasting in China, and constructs a hybrid forecasting model based on the group method of dot selective ensemble. It mainly focuses on predicting the nonlinear variation of energy consumption. The model first predicts the linear trend of energy consumption time series through the group method of data handling-based autoregressive model and then obtains the residual subseries of energy consumption. Considering the highly nonlinear characteristics of the residual subseries, this study introduces AdaBoost ensemble technology to enhance the forecasting performance of the single nonlinear prediction model, back propagation neural network, support vector regression machine, genetic programming, and radical basis function neural network respectively, to obtain four different versions of the ensemble model on nonlinear subseries. Further, the prediction results of these four AdaBoost ensemble models are used as an initial input, and the selective combination prediction for the nonlinear subseries is obtained by using the group method of data handling. Finally, two parts are added up to obtain the final prediction. The empirical analysis of total energy consumption and total oil consumption in China shows that the forecasting performance of the proposed model is better than that of the group method of data handling based autoregressive model and seven other hybrid models, and this study gives the out-of-sample forecasting of two time series from 2015 to 2020. (C) 2018 Elsevier Ltd. All rights reserved.