forecasting china’s coal power installed capacity: a comparison of mgm, arima, gm-arima, and nmgm models
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2018
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Abstract
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.
| Reference Key |
li2018sustainabilityforecasting
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|---|---|
| Authors | ;Shuyu Li;Xue Yang;Rongrong Li |
| Journal | journal of physics: conference series |
| Year | 2018 |
| DOI |
10.3390/su10020506
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