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Predicting measurements at unobserved locations in an electrical transmission system

Author

Listed:
  • Dirk Surmann

    (TU Dortmund University)

  • Uwe Ligges

    (TU Dortmund University)

  • Claus Weihs

    (TU Dortmund University)

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.

Suggested Citation

  • Dirk Surmann & Uwe Ligges & Claus Weihs, 2018. "Predicting measurements at unobserved locations in an electrical transmission system," Computational Statistics, Springer, vol. 33(3), pages 1159-1172, September.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:3:d:10.1007_s00180-017-0734-2
    DOI: 10.1007/s00180-017-0734-2
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    Cited by:

    1. Hans A. Kestler & Bernd Bischl & Matthias Schmid, 2018. "Proceedings of Reisensburg 2014–2015," Computational Statistics, Springer, vol. 33(3), pages 1125-1126, September.

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