An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China

Abstract

With rapid development of wind power in China, it has become an integral part of the energy structure, so it is of practical significance to forecast wind power generation accurately. As wind power generation in China experiences both an exponential increase trend and a seasonal fluctuation pattern, it cannot be accurately forecasted by traditional models. As the grey model GM(1,1) can capture an exponential growth trend and the Hodrick-Prescott filter is known for its capability of characterizing seasonality factors, this paper proposes a novel seasonal forecasting method that integrates the HP filter into the grey model GM(1,1). The proposed model is then applied to carry out an empirical analysis based on the seasonal wind power generation data between 2013 and 2019 in China. The forecasting results from the new model are then compared with three existing approaches. The comparison results indicate that the proposed model generally outperforms existing methods as it can well capture seasonal fluctuations in the data series. A further prediction of wind power generation in China is conducted into a future horizon of 2020 and 2021 by using our model, followed by a set of policy recommendations for further development of the wind power industry in China.