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Electrical Data Matrix Decomposition in Smart Grid

Qian Dang1, Huafeng Zhang1, Bo Zhao2, Yanwen He2, Shiming He3,*, Hye-Jin Kim4
Information & Communication Corporation, State Grid Gansu Electric Power Company, Lanzhou, 730050, China.
State Grid Gansu Electric Power Corporation, Lanzhou, 730050, China.
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
Business Administration Research Institute, Sungshin W. University, 02844, Korea.
*Corresponding Author: Shiming He. Email: .

Journal on Internet of Things 2019, 1(1), 1-7. https://doi.org/10.32604/jiot.2019.05804

Abstract

As the development of smart grid and energy internet, this leads to a significant increase in the amount of data transmitted in real time. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation of electricity data which is generated by human’s actions and feelings, we build a low-rank electricity data matrix where the row is time and the column is user. Inspired by matrix decomposition, we divide the low-rank electricity data matrix into the multiply of two small matrices and use the known data to approximate the low-rank electricity data matrix and recover the missed electrical data. Based on the real electricity data, we analyze the low-rankness of the electricity data matrix and perform the Matrix Decomposition-based method on the real data. The experimental results verify the efficiency and efficiency of the proposed scheme.

Keywords

Electrical data recovery, matrix decomposition, low-rankness, smart grid

Cite This Article

Q. Dang, H. Zhang, B. Zhao, Y. He, S. He et al., "Electrical data matrix decomposition in smart grid," Journal on Internet of Things, vol. 1, no.1, pp. 1–7, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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