# Recovery of Missing Data in Correlated Smart Grid Datasets

## TL;DR

This paper investigates the recovery of missing data in correlated smart grid datasets using matrix completion, demonstrating that Bayesian SVT outperforms standard SVT by effectively exploiting dataset correlations.

## Contribution

It introduces a framework for joint recovery of correlated datasets in smart grids and evaluates the performance of BSVT, showing its superiority over SVT in this context.

## Key findings

- BSVT outperforms SVT in data recovery accuracy.
- Correlation between datasets enhances recovery performance.
- Fundamental limits characterize the potential of joint data recovery.

## Abstract

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.