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Review of Power Spatio-Temporal Big Data Technologies, Applications, and Challenges

  • Ying Ma
  • Chao HuangEmail author
  • Yu Sun
  • Guang Zhao
  • Yunjie Lei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)

Abstract

The spatio-temporal big data of the power grid has experienced explosive growth, especially the development of various power sensors, smart devices, communication devices, and real-time processing hardware, which has led to unprecedented opportunities and challenges in this field. This paper firstly introduces Power Spatio-Temporal Big Data (PSTBD) technologies based on the characteristics of grid spatio-temporal big data, followed by a comprehensive survey of relevant articles analysis in this field. Then we compare the difference between traditional power grid and PSTBD platform, and focus on the key technologies of current PSTBD and corresponding typical applications. Finally, the development direction and challenges of PSTBD are given. Through data analysis and technical discussion, we provided technical supports and decision supports for relevant practitioners in PSTBD field.

Keywords

Spatio-temporal Security control Power grid Big data Sensors 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61502404), Natural Science Foundation of Fujian Province of China (Grant No. 2019J01851), Distinguished Young Scholars Foundation of Fujian Educational Committee (Grant No. DYS201707), Xiamen Science and Technology Program (Grant No. 3502Z20183059), and Open Fund of Key Laboratory of Data mining and Intelligent Recommendation, Fujian Province University. We thank the anonymous reviewers for their great helpful comments.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ying Ma
    • 1
  • Chao Huang
    • 2
    Email author
  • Yu Sun
    • 3
  • Guang Zhao
    • 2
  • Yunjie Lei
    • 1
  1. 1.Xiamen University of TechnologyXiamenChina
  2. 2.Xiamen Great Power GeoInformation Technology Co., Ltd.XiamenChina
  3. 3.National Tsing Hua UniversityHsinchuTaiwan

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