Recovery of Missing Data in Correlated Smart Grid Datasets

@article{Genes2019RecoveryOM,
  title={Recovery of Missing Data in Correlated Smart Grid Datasets},
  author={Cristian Genes and I{\~n}aki Esnaola and Samir Medina Perlaza and Daniel Coca},
  journal={2019 IEEE Data Science Workshop (DSW)},
  year={2019},
  pages={263-269}
}
We study the recovery of missing data from multiple smart grid datasets within a matrix completion framework. The datasets contain the electrical magnitudes required for monitoring and control of the electricity distribution system. Each dataset is described by a low rank matrix. Different datasets are correlated as a result of containing measurements of different physical magnitudes generated by the same distribution system. To assess the validity of matrix completion techniques in the… 

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