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

机译：基于HP滤波器的改进的季节性通用术（1,1）模型预测中国风力发电

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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.
机译：随着中国风力发动的快速发展，它已成为能源结构的一部分，因此预测风力发电是实际意义。随着中国风力发电经历指数增加趋势和季节性波动模式，传统模型无法准确预测。由于灰色模型GM（1,1）可以捕获指数增长趋势，并且Hodrick-Prescott过滤器以其表征季节性因素的能力而已知，提出了一种新的季节性预测方法，将HP滤波器集成到灰色模型通用镜头中（1,1）。然后应用所提出的模型来基于2013年和2019年在中国之间的季节风发电数据进行实证分析。然后将来自新模型的预测结果与三种现有方法进行比较。比较结果表明，所提出的模型通常优于现有的方法，因为它可以很好地捕获数据系列中的季节性波动。通过使用我们的模型，在中国风力发电的进一步预测到了2020年和2021年的未来地平线，其次是一系列用于进一步发展中国风力电力行业的政策建议。

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