Big Data Analytics in Smart Power Systems: A Survey Paper
Abstract
Abstract Views: 157
In recent years, technology has brought us many advancements. One of them is integrating big data with smart grid/smart power. In this study, a scientific approach used to help the power system is studied. Additionally, with the help of previously published literature, different survey papers are reviewed to investigate the key challenges of integrating Big Data Analytics (BDA) with smart grid. Subsequently, BDA characteristics are also studied. Next, data analysis techniques and BDA applications in the domain of smart grids are studied. It is followed by a section discussing techniques such as Hadoop and Spark. Their framework is also briefly examined in order to know about their working. The last section provides a conclusion and future directions.
KEYWORDS: advanced metering infrastructure (AMI), applications of big data, big data analytics (BDA), data architectures, data mining, data privacy, data security, data uncertainty, data volume, hadoop, spark, Vz of the data, smart grid, smart power system big
Downloads
References
H. Akhavan-Hejazi and H. Mohsenian-Rad, "Power systems big data analytics: An assessment of paradigm shift barriers and prospects," Energy Reports, vol. 4, pp. 91-100, 2018.
B. P. Bhattarai, S. Paudyal, Y. Luo, M. Mohanpurkar, K. Cheung, R. Tonkoski, et al., "Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions," IET Smart Grid, vol. 2, pp. 141-154, 2019.
C. K. Emani, N. Cullot, and C. Nicolle, "Understandable big data: a survey," Computer science review, vol. 17, pp. 70-81, 2015.
S. Halford and M. Savage, "Speaking sociologically with big data: Symphonic social science and the future for big data research," Sociology, vol. 51, pp. 1132-1148, 2017.
A. Munshi and A.-R. M. Yasser, "Big data framework for analytics in smart grids," Electric Power Systems Research, vol. 151, pp. 369-380, 2017.
Y. Zhang, T. Huang, and E. F. Bompard, "Big data analytics in smart grids: a review," Energy informatics, vol. 1, pp. 1-24, 2018.
Y. Zomaya and Y. C. Lee, Energy-efficient distributed computing systems: John Wiley & Sons, 2012.A.Züfle, G. Trajcevski, D. Pfoser, and J.-S. Kim, "Managing uncertainty in evolving geo-spatial data," in 2020 21st IEEE International Conference on Mobile Data Management (MDM), 2020, pp. 5-8.
Y. Simmhan, S. Aman, A. Kumbhare, R. Liu, S. Stevens, Q. Zhou, et al., "Cloud-based software platform for big data analytics in smart grids," Computing in Science & Engineering, vol. 15, pp. 38-47, 2013.
M. Kezunovic, P. Pinson, Z. Obradovic, S. Grijalva, T. Hong, and R. Bessa, "Big data analytics for future electricity grids," Electric Power Systems Research, vol. 189, p. 106788, 2020.
E. Hossain, I. Khan, F. Un-Noor, S. S. Sikander, and M. S. H. Sunny, "Application of big data and machine learning in smart grid, and associated security concerns: A review," Ieee Access, vol. 7, pp. 13960-13988, 2019.
UMT-AIR follow an open-access publishing policy and full text of all published articles is available free, immediately upon publication of an issue. The journal’s contents are published and distributed under the terms of the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Thus, the work submitted to the journal implies that it is original, unpublished work of the authors (neither published previously nor accepted/under consideration for publication elsewhere). On acceptance of a manuscript for publication, a corresponding author on the behalf of all co-authors of the manuscript will sign and submit a completed the Copyright and Author Consent Form.
