Skip to main content

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

  • Conference paper
  • First Online:
Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Rob, P., Sean, D., Robert, G., et al.: Interpreting the dataparallel analysis with Sawzall. Sci. Program. 13(4), 277–298 (2005)

    Google Scholar 

  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. 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). https://doi.org/10.1007/978-3-319-32025-0_9

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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). S0167739X17318769

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chao Huang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ma, Y., Huang, C., Sun, Y., Zhao, G., Lei, Y. (2019). Review of Power Spatio-Temporal Big Data Technologies, Applications, and Challenges. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11637. Springer, Cham. https://doi.org/10.1007/978-3-030-24900-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24900-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24899-4

  • Online ISBN: 978-3-030-24900-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics