Saudi Arabia has recently begun promoting renewable energy as a potential alternative to fossil fuels for domestic power generation. In order to efficiently connect wind energy to the existing power grids, reliable wind forecasts and an accurate way of quantifying the uncertainties of these forecasts are required. Motivated by a data set of hourly wind speeds from 28 stations in Saudi Arabia, we build spatiotemporal models for short-term probabilistic forecasts of wind vectors. Traditionally, wind speed and wind direction have been considered independently, without taking dependencies into account. However, in many situations, for example, energy management, it is essential to have information on the bivariate nature of the wind. We compare a coregionalization model for the wind vector with a univariate spatiotemporal model for the transformed wind speed in terms of sharpness and calibration. In both cases the linear predictor is a function of covariates, a smooth function to capture the daily seasonality in the wind and a latent Gaussian field to model the spatial and temporal dependencies. Substantial improvements in reliability are observed when modelling the full bivariate structure instead of only considering speed. Furthermore, the bivariate model has the advantage of also producing forecasts for the wind direction. A Bayesian framework is used to obtain forecasts that are accurate and reliable, even at stations without observations, with relatively low computational cost. Simulated high-resolution data from a computer model are used to validate spatiotemporal forecasts. A detailed analysis on this case study shows how increasing the number of locations can improve the forecast performance.

中文翻译：

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沙特阿拉伯的时空概率风矢量预测

沙特阿拉伯最近已开始推广可再生能源，以作为国内发电中化石燃料的潜在替代品。为了有效地将风能连接到现有电网，需要可靠的风能预测以及量化这些预测的不确定性的准确方法。根据沙特阿拉伯28个站点的每小时风速数据集，我们建立了时空模型，用于风速矢量的短期概率预测。传统上，风速和风向是独立考虑的，没有考虑相关性。但是，在许多情况下，例如能源管理，获取有关风的双变量性质的信息至关重要。我们在锐度和标定方面比较了风矢量的共区域化模型和转换风速的单变量时空模型。在这两种情况下，线性预测器都是协变量的函数，是捕获风中每日季节性变化的平滑函数，并且是对空间和时间相关性进行建模的潜在高斯场。在对完整的双变量结构建模时，不仅考虑速度，还观察到了可靠性的显着提高。此外，双变量模型的优势还在于可以生成风向预测。贝叶斯框架用于以相对较低的计算成本获得准确而可靠的预测，即使在没有观测站的情况下也是如此。来自计算机模型的模拟高分辨率数据用于验证时空预测。对这个案例研究的详细分析显示，增加位置数量可以如何改善预测性能。