  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 recovery of missing data, we characterize
the fundamental limits when two correlated datasets are jointly recovered. We
then proceed to evaluate the performance of Singular Value Thresholding (SVT)
and Bayesian SVT (BSVT) in this setting. We show that BSVT outperforms SVT by
simulating the recovery for different correlated datasets. The performance of
BSVT displays the tradeoff behaviour described by the fundamental limit, which
suggests that BSVT exploits the correlation between the datasets in an
efficient manner.
