Genes, C., Esnaola, I., Perlaza, S. et al. (1 more author) (2019) Recovery of missing data in correlated smart grid datasets. In: 2019 IEEE Data Science Workshop (DSW). 2019 IEEE Data Science Workshop (DSW), 02-05 Jun 2019, Minneapolis, MN, USA. IEEE , pp. 263-269. ISBN 978-1-7281-0708-0
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. This is an author-produced version of a paper subsequently published in 2019 IEEE Data Science Workshop (DSW). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | smart grid; matrix completion; missing data recovery; correlated data |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Jul 2019 11:46 |
Last Modified: | 04 Jul 2020 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/DSW.2019.8755592 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149034 |