Short-term wind speed prediction is an essential task for wind resource and wind energy planning. However, most of this literature does not take into account the spatio-termporal correlation of wind data from the geographical field. For this reason, we propose an integrated spatio-temporal kriging and functional kriging strategy to exploit such spatio-temporal correlation into the wind speed prediction. First, the deterministic trend component in wind data is estimated to be removed. The residuals are used for spatio-temporal modeling and prediction. Based on the spatio-temporal kriging framework, four spatio-temporal covariance models (product-sum model, separable exponential product model, separable and nonseparable Gneiting models) are considered which describe the spatio-temporal correlation of wind data. In particular, the flexibility of using the nonseparable Gneiting model is highlighted. More specifically, four spatio-temporal random fields are modeled from the 12 wind monitoring stations over Ireland. We also use an involved weighted least squares method for estimating parameters of the four covariance models involved in the spatio-temporal kriging strategy. We apply the fitted covariance models to generate day-ahead wind speed predictions at both observed and nonobserved locations where wind station already exist but also to nearby locations. Leave-one-out cross-validation is applied to check the significance of the difference among the four models, these spatio-temporal ordinary kriging (STOK), functional ordinary kriging (FOK) and autoregressive integrated moving average (ARIMA) methods are compared for day-ahead wind speed predictions. Forecasting results indicate that the predicting accuracy is improved almost 33.5% using FOK compared with three approaches which confirm the effectiveness of the functional kriging method in the paper.

中文翻译：

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基于时空地统计方法和克里金法的短期风速预测

短期风速预测是风资源和风能规划的一项重要工作。然而，这些文献中的大部分都没有考虑到地理场风数据的时空相关性。出于这个原因，我们提出了一种集成的时空克里金和功能克里金策略，以利用这种时空相关性来预测风速。首先，估计要去除风数据中的确定性趋势分量。残差用于时空建模和预测。基于时空克里金框架，考虑描述风数据时空相关性的四种时空协方差模型（积和模型、可分离指数积模型、可分离和不可分离Gneiting模型）。特别是，强调了使用不可分离 Gneiting 模型的灵活性。更具体地说，四个时空随机场是从爱尔兰上空的 12 个风监测站模拟出来的。我们还使用涉及的加权最小二乘法来估计时空克里金策略中涉及的四个协方差模型的参数。我们应用拟合的协方差模型在已经存在风站的观测和未观测位置以及附近位置生成日前的风速预测。留一法交叉验证用于检查四种模型之间差异的显着性，比较了这些时空普通克里金法（STOK）、泛函普通克里金法（FOK）和自回归积分移动平均法（ARIMA）方法日前的风速预测。