Spatio-temporal asymmetry of local wind fields and its impact on short-term wind forecasting.
Spatio-temporal asymmetry of local wind fields and its impact on short-term wind forecasting.
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DOI：
10.1109/tste.2018.2789685
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发表时间：
2018-07
影响因子：
8.8
通讯作者：
Ding Y
中科院分区：
文献类型：
--
作者：
Ezzat AA;Jun M;Ding Y
当今风电场收集的大量时空数据使得精确的时空模型成为必要。尽管不可分离的时空模型日益受到认可，但在当今的可再生能源行业中，对可分离的对称模型的严重依赖仍然是常态。我们发现可分离模型的广泛使用是由于在一种无法揭示其精细时空结构的环境中处理风力数据。这项研究的贡献有两方面。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，随后我们得出结论，局部风场呈现出强烈的不可分离性和不对称性迹象。利用一年针对特定涡轮机的风力测量数据，我们表明实际上在超过93%的时间里都能检测到不对称性。其次，利用时空透镜，我们提出了一种用于短期风速预测的改进方法。在风速和风力的预测精度方面都观察到了显著提高。当与某些智能方法（如支持向量机）结合时，可能会有进一步的改进。
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 non-separable 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 two-fold. 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 non-separability 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.