Construction of new coal-fired power plants in China has posed a huge challenge to energy sustainability. Forecasting the installed capacity more accurately can serve to develop better energy sustainability strategy. A comparison between linear and non-linear forecasting models can more comprehensively describe the characteristics of the prediction data and provide multi-angle analysis of the prediction results. In this paper, we develop four time-series forecasting techniques-metabolism grey model (MGM), autoregressive integrated moving average (ARIMA), grey model (GM)-ARIAM, and nonlinear metabolism grey model (NMGM)-for better forecasting of coal-fired power installed capacity. The average relative errors between the simulation and actual data of the MGM, GM-ARIMA, ARIMA, and NMGM model are 3.37%, 2.13%, 3.71% and 2.36% respectively, which indicate those four models can produce highly accurate results. The forecasting results show the average annual growth rate of China's coal-fired power installed capacity in the next ten years (2017-2016) will be 5.26% a year, which is slower than the average annual growth rate (8.20% a year) for 2007-2016. However, the average annual new added installed capacity for 2017-2026 will be 74 gigawatts, which is higher than the average annual added installed capacity (56 gigawatts) for 2007-2016. © 2018 by the authors.
