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)


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.


Spatio-temporal Security control Power grid Big data Sensors 



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.


  1. 1.
    Liu, D., Xu, P., Ren, L.: TPFlow: progressive partition and multidimensional pattern extraction for large-scale spatio-temporal data analysis. IEEE Trans. Vis. Comput. Graph. 25(1), 1–11 (2019)CrossRefGoogle Scholar
  2. 2.
    Lu, M., Pebesma, E., Sanchez, A., Verbesselt, J.: Spatio-temporal change detection from multidimensional arrays: detecting deforestation from MODIS time series. ISPRS J. Photogram. Remote Sens. 117, 227–236 (2016)CrossRefGoogle Scholar
  3. 3.
    Idehen, I., Wang, B., Shetye, K., Overbye, T., Weber, J.: Visualization of large-scale electric grid oscillation modes. In: 2018 IEEE North American Power Symposium (NAPS), pp. 1–6 (2018)Google Scholar
  4. 4.
    Li, Y., Wang, Z., Hao, Y.: A hierarchical visualization analysis model of power big data. In: IOP Conference Series: Earth and Environmental Science, vol. 108, no. 5, pp. 52–64 (2018)CrossRefGoogle Scholar
  5. 5.
    Yu, N., Shah, S., Johnson, R., Sherick, R., Hong, M., Loparo, K.: Big data analytics in power distribution systems. In: 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2015)Google Scholar
  6. 6.
    Sadiq, B., et al.: A spatio-temporal multimedia big data framework for a large crowd. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2742–2751 (2015)Google Scholar
  7. 7.
    Cai, H., Xu, B., Jiang, L., Vasilakos, A.V.: IoT-based big data storage systems in cloud computing: perspectives and challenges. IEEE Internet Things J. 4(1), 75–87 (2017)Google Scholar
  8. 8.
    Zhong, R.Y., Newman, S.T., Huang, G.Q., Lan, S.: Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput. Ind. Eng. 101, 572–591 (2016)CrossRefGoogle Scholar
  9. 9.
    Tao, F., Cheng, J., Qi, Q., et al.: Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94(9–12), 3563–3576 (2018)CrossRefGoogle Scholar
  10. 10.
    He, X., Ai, Q., Qiu, R.C., Huang, W., Piao, L., Liu, H.: A big data architecture design for smart grids based on random matrix theory. IEEE Trans. Smart Grid 8(2), 674–686 (2017)Google Scholar
  11. 11.
    Thusoo, A., Sarma, J., Jain, N., et al.: Hive warehousing solution over map-reduce framework. In: Proceedings of the 35th International Conference on Very Large Data Bases (VLDB), Lyon, France, pp. 1626–1629. VLDB (2009)CrossRefGoogle Scholar
  12. 12.
    Christopher, O., Benjamin, R., Utkarsh, S.: Pig Latina not-so-foreign language for data processing. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, pp. 1099–1110. ACM (2008)Google Scholar
  13. 13.
    Rob, P., Sean, D., Robert, G., et al.: Interpreting the dataparallel analysis with Sawzall. Sci. Program. 13(4), 277–298 (2005)Google Scholar
  14. 14.
    Prahlad, A., Gokhale, P., Kottomtharayil, R., et al.: Data mining systems and methods for heterogeneous data sources. U.S. Patent 9,405,632 (2016)Google Scholar
  15. 15.
    Kong, C., Gao, M., Xu, C., Qian, W., Zhou, A.: Entity matching across multiple heterogeneous data sources. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 133–146. Springer, Cham (2016). Scholar
  16. 16.
    Wang, Y., Chen, Q., Kang, C., et al.: Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Trans. Smart Grid 7(5), 2437–2447 (2017)CrossRefGoogle Scholar
  17. 17.
    Marinakis, V., Doukas, H., Tsapelas, J., et al.: From big data to smart energy services: an application for intelligent energy management. Future Gener. Comput. Syst. (2018). S0167739X17318769Google Scholar
  18. 18.
    Shi, H., Xu, M., Ran, L.: Deep learning for household load forecasting novel pooling deep RNN. IEEE Trans. Smart Grid 99(1), 1 (2017)Google Scholar
  19. 19.
    Guo, B., Liu, Y., Ouyang, Y., et al.: Harnessing the power of the general public for crowdsourced business intelligence: a survey. IEEE Access 7, 26606–26630 (2019)CrossRefGoogle Scholar
  20. 20.
    Zhang, Y., Wang, J.: A distributed approach for wind power probabilistic forecasting considering spatio-temporal correlation without direct access to off-site information. IEEE Trans. Power Syst. 33(5), 5714–5726 (2018)CrossRefGoogle Scholar
  21. 21.
    Wang, J., Wang, Y., Zhang, D., et al.: Energy saving techniques in mobile crowd sensing: current state and future opportunities. IEEE Commun. Mag. 56(5), 164–169 (2018)CrossRefGoogle Scholar
  22. 22.
    Hossain, E., Khan, I., Un-Noor, F., et al.: Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access 7, 13960–13988 (2019)CrossRefGoogle Scholar

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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