The massive amounts of spatio-temporal data collected in today's wind farms have created a necessity for accurate spatio-temporal models. Despite the growing recognition for nonseparable spatio-temporal models, a significant reliance on separable, symmetric models is still the norm in today's renewable industry. We discover that the broad use of separable models is due to the handling of wind data in a setting that does not reveal their fine-scale spatio-temporal structure. The contribution of this research is twofold. First, we devise a special pair of spatio-temporal “lens” that allows us to see the fine-scale spatio-temporal variations and interactions, and subsequently, we conclude that local wind fields exhibit strong signs of nonseparability and asymmetry. Using one year of turbine-specific wind measurements, we show that asymmetry can, in fact, be detected in more than 93% of the time. Second, making use of the spatio-temporal lens, we propose an enhanced procedure for short-term wind speed forecast. Substantial improvements in forecast accuracy in both wind speed and wind power were observed. When combined with certain intelligent methods such as support vector machine, additional improvements are possible.

中文翻译：

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局部风场的时空非对称性及其对短期风预报的影响

当今风电场中收集的大量时空数据已成为建立精确时空模型的必要条件。尽管人们越来越认识到不可分的时空模型，但在当今可再生能源行业中，仍然严重依赖可分离的对称模型。我们发现，可分离模型的广泛使用是由于在不揭示其精细时空结构的环境中对风数据的处理。这项研究的贡献是双重的。首先，我们设计了一对特殊的时空“透镜”，使我们能够看到精细的时空变化和相互作用，然后，我们得出结论，当地风场表现出不可分离和不对称的强烈迹象。使用一年的涡轮机特定风量测量，我们表明，实际上，不对称现象可以在超过93％的时间内被检测到。其次，利用时空透镜，我们提出了一种增强的短期风速预报程序。观察到风速和风能的预报准确性均得到了显着提高。当与某些智能方法（如支持向量机）结合使用时，可能会进行其他改进。