Predicting measurements at unobserved locations in an electrical transmission system

Dirk Surmann (), Uwe Ligges and Claus Weihs
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Dirk Surmann: TU Dortmund University
Uwe Ligges: TU Dortmund University
Claus Weihs: TU Dortmund University

Computational Statistics, 2018, vol. 33, issue 3, No 4, 1159-1172

Abstract: Abstract 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.

Keywords: Electrical network; Predict unobserved locations; Graph kernel (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-017-0734-2

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