Big Data Compression in Smart Grids via Optimal Singular Value Decomposition

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Hashemipour, SN (Author) Other; Aghaei, J (Author) Other; Kavousi fard, A (Author) Other; Niknam, T (Author) Other; Salimi, L (Author) Other; del Granado, PC (Author) Other; Shafie khah, M (Author) Other; Wang, F (Author) Other; Catalao, JPS (Author) FEUP

Conference proceedings International

Title: IEEE Industry Applications Society Annual Meeting, IAS 2020, Detroit, MI, USA, October 10-16, 2020

Pages: 1-8

2020 IEEE Industry Applications Society Annual Meeting, IAS 2020

10 October 2020 through 16 October 2020

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00T-GVB

DOI: 10.1109/ias44978.2020.9334900

Abstract (EN): The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. With the rapid development of smart grids, smart meters and sensors are used to monitor the system and provide a wide reporting which produce a huge amount of data in various part of the grid. To logical manage this trouble, the presented paper proposes a new lossy data compression approach for big data compression. In the proposed method, at the first step, the optimal singular value decomposition (OSVD) is applied to a matrix that achieves the optimal number of singular values to the sending process and the other ones will be neglected. This goal is done due to the quality of retrieved data and the rate of compression ratio. In the presented scheme, to implementation of the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using the experimental dataset of several residential microgrid consumers and market dataset. Simulation results show the high performance and efficiency of the proposed model in smart grids with big data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8