Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models
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.
Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models
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.
Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models
Shuyu Li (Autor:in) / Xue Yang (Autor:in) / Rongrong Li (Autor:in)
2018
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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