Predicting measurements at unobserved locations in an electrical transmission system
Predicting measurements at unobserved locations in an electrical transmission system
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DOI：
10.1007/s00180-017-0734-2
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发表时间：
2017-05
影响因子：
1.3
通讯作者：
D. Surmann;U. Ligges;C. Weihs
中科院分区：
文献类型：
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作者：
D. Surmann;U. Ligges;C. Weihs
电力传输系统由大量具有不同类型测量的位置（节点）组成。我们的目标是导出节点子集，其中包含几乎足够的信息来描述整个能源网络。我们得出一个参数集，分别表征每个测量位置或节点。通过分析每个节点相对于其邻居的行为，我们在整个传输系统上构建了一个可行的随机场元模型。元模型用于平滑整个网络的测量。在下一步中，我们使用位置的子集来预测未观察到的位置。我们推导出不同的图核来定义网络邻域结构中缺失的协方差矩阵。这产生了一个元模型，该元模型能够使用非各向同性距离函数平滑观察到的位置并预测空间域中未观察到的位置。
Electrical transmission systems consist of a huge number of locations (nodes) with different types of measurements available. Our aim is to derive a subset of nodes which contains almost sufficient information to describe the whole energy network. We derive a parameter set which characterises every single measuring location or node, respectively. Via analysing the behaviour of each node with respect to its neighbours, we construct a feasible random field metamodel over the whole transmission system. The metamodel is used to smooth the measurements across the network. In the next step we work with a subset of locations to predict the unobserved ones. We derive different graph kernels to define the missing covariance matrix from the neighbourhood structures of the network. This results in a metamodel that is able to smooth observed and predict unobserved locations in a spatial domain with non-isotropic distance functions.