Spatio-temporal asymmetry of local wind fields and its impact on short-term wind forecasting.
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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.