Predicting measurements at unobserved locations in an electrical transmission system

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.