It is essential to enhance the ability of wind speeds forecasting for wind energy and wind resource planning. For this purpose, a hybrid strategy has been proposed based on spatio-temporal covariance model which combined the spatio-temporal ordinary kriging (STOK) technology with autoregressive integrated moving average (ARIMA) regression smoothing method. This is because wind speed time series exhibits a long-term dependency. In the case study, both STOK method and ARIMA method are employed and their performances are compared. The ARIMA model can obtain a necessary and sufficient smoothing condition for them to be smoothed. Meanwhile, further theoretical analysis is provided to discuss why the STOK method is potentially more accurate than the ARIMA method for wind speed time series prediction. Results show that the proposed method outperforms the Non-Sep-Gneiting model by 9% and 7.2% in terms of mean absolute error (MAE) and root-mean-square error (RMSE).

中文翻译：

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基于自回归综合移动平均回归平滑的时空协方差模型风速预测

提高风速预报能力对于风能和风资源规划至关重要。为此，提出了一种基于时空协方差模型的混合策略，该模型将时空普通克里金（STOK）技术与自回归积分移动平均（ARIMA）回归平滑方法相结合。这是因为风速时间序列表现出长期依赖性。在案例研究中，采用了 STOK 方法和 ARIMA 方法，并比较了它们的性能。ARIMA 模型可以为它们得到平滑的充分必要平滑条件。同时，进一步的理论分析讨论了为什么STOK方法可能比ARIMA方法更准确地预测风速时间序列。