Spatiotemporal interpolation and forecast of irradiance data using Kriging
Spatiotemporal interpolation and forecast of irradiance data using Kriging
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DOI：
10.1016/j.solener.2017.09.057
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发表时间：
2017-12
期刊：
影响因子：
6.7
通讯作者：
M. Jamaly;J. Kleissl
中科院分区：
文献类型：
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作者：
M. Jamaly;J. Kleissl
太阳能发电的可变性是电网运营商关注的一个问题，因为光伏电站输出功率的意外变化可能会使电网紧张。太阳变化的主要原因是经过光伏组件的云层。然而，一个区域的地理多样性导致云引起的变率减少。本文对辐照度数据的时空相关性进行了分析，并在给定观测点辐照时间序列的基础上，应用时空普通克里格方法对任意点辐照进行了建模。观测点辐照度之间的相关性用一般参数协方差函数建模。除了各向同性（与方向无关）协方差函数外，还提出了一种新的非可分各向异性参数协方差函数来模拟瞬态云。此外，提出了一种利用参数协方差函数分析估计时空去相关距离的新方法，在不损失精度的情况下减少了计算量。利用大涡模拟生成的两个空间和时间分辨的人工辐照度数据集，对Kriging方法进行了分析和验证。然后，将时空Kriging方法应用于加利福尼亚州（萨克拉门托和圣地亚哥地区）的实际辐照度和输出功率数据，在此过程中必须使用相互关联方法（CCM）估计云的运动。结果证实，各向异性模型最准确，平均归一化均方根误差（nRMSE）为7.92%，比持久性模型相对提高66%。
Solar power variability is a concern to grid operators as unanticipated changes in PV plant power output can strain the electric grid. The main cause of solar variability is clouds passing over PV modules. However, geographic diversity across a region leads to a reduction in the cloud-induced variability. In this paper, spatiotemporal correlations of irradiance data are analyzed and spatial and spatiotemporal ordinary Kriging methods are applied to model irradiation at an arbitrary point based on the given time series of irradiation at some observed locations. The correlations among the irradiances at observed locations are modeled by general parametric covariance functions. Besides the isotropic covariance function (which is independent of direction), a new non-separable anisotropic parametric covariance function is proposed to model the transient clouds. Also, a new approach is proposed to estimate the spatial and temporal decorrelation distances analytically using the applied parametric covariance functions, which reduce the size of the computations without loss in accuracy (parameter shrinkage). The analysis has been performed and the Kriging method is first validated by using two spatially and temporally resolved artificial irradiance datasets generated from Large Eddy Simulation. Then, the spatiotemporal Kriging method is applied on real irradiance and output power data in California (Sacramento and San Diego areas) where the cloud motion had to be estimated during the process using cross-correlation method (CCM). Results confirm that the anisotropic model is most accurate with an average normalized root mean squared error (nRMSE) of 7.92% representing a 66% relative improvement over the persistence model.