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Radiation

机译:辐射

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摘要

Several variants of spatio-temporal kriging are used to perform very short-term solar irradiance forecasting by utilizing data from a sensor network. Kriging can produce forecasts not only at the locations of the irradiance monitoring stations, but also at locations where sensors are not installed. Leave-one-out cross-validation is used to test the kriging performance at unobserved locations. Kriging weights are determined either empirically or using a correlation function. Four parametric correlation functions (correlograms) are herein considered, namely, separable, fully symmetric, and two polynomial-adjusted correlation functions. A dense 1 km × 1.2 km network of 17 stations located on Oahu island, Hawaii, is used in this paper. We find that kriging based on a polynomial-adjusted correlation function (the best among the parametric models) is able to obtain forecast skill up to 0.43 and 0.36 for observed and unobserved locations respectively, for a forecast horizon of 50 s. It is also found that empirical kriging performs better than parametric models at small forecast horizons (such as 30 s). However, it loses accuracy for forecast horizons longer than 100 s.
机译:时空克里金法的几种变体用于通过利用来自传感器网络的数据来执行非常短期的太阳辐照度预测。克里金法不仅可以在辐照监测站的位置,而且可以在未安装传感器的位置产生预测。留一法交叉验证用于在未观察到的位置测试克里金法性能。克里格权重可以凭经验或使用相关函数确定。这里考虑四个参数相关函数(相关图),即,可分离的,完全对称的和两个多项式调整的相关函数。本文使用了位于夏威夷瓦胡岛的由17个站点组成的1 km×1.2 km的密集网络。我们发现,基于多项式调整的相关函数(参数模型中最好的)的克里金法能够在50 s的预测范围内分别获得0.43和0.36的观测技能,分别用于观测和未观测位置。还发现,在较小的预测范围(例如30 s)下,经验克里金法比参数模型的性能更好。但是,对于超过100 s的预测范围,它将失去准确性。

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  • 来源
    《Oceanographic Literature Review》 |2016年第2期|253-253|共1页
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