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Data Analytic for Improving Operations and Maintenance in Smart-Grid Environment

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IoT for Smart Grids

Part of the book series: Power Systems ((POWSYS))

Abstract

The Smart-Grid concept relies on a collection of generation, transmission and distribution components that undertake power production and delivery to various types of loads. Since multiple components have to be collaborated in this procedure, advanced system orchestrators are absolutely necessary. The decision of these intelligent mechanism typically rely on the analysis of large amount of data, also known as “big data analytic”, in order to optimize among others the environmental and economic constraints. This chapter provides an overview of recent advances in the domain of big data analytic, which are suitable for being applied to the smart-grid environment.

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References

  1. Big data (2008). http://www.nature.com/news/specials/bigdata/index.html

  2. Special online collection: dealing with big data (2011). http://www.sciencemag.org/site/special/data/

  3. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010). https://doi.org/10.1145/1721654.1721672

    Article  Google Scholar 

  4. Berry, M.W.: Survey of Text Mining. Springer, Berlin (2003)

    Google Scholar 

  5. Calheiros, R.N., Ranjan, R., Buyya, R.: Virtual machine provisioning based on analytical performance and QoS in cloud computing environments. In: 2011 International Conference on Parallel Processing, pp. 295–304 (2011). https://doi.org/10.1109/ICPP.2011.17

  6. Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014). https://doi.org/10.1007/s11036-013-0489-0

    Article  Google Scholar 

  7. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008). https://doi.org/10.1145/1327452.1327492

    Article  Google Scholar 

  8. Derby, O.C.: FlexGP : a scalable system for factored learning in the cloud (2013). http://hdl.handle.net/1721.1/85216

  9. Dietterich, T.G.: Ensemble methods in machine learning. Multiple Classifier Systems, pp. 1–15. Springer, Berlin (2000)

    Google Scholar 

  10. Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the World-Wide Web. Commun. ACM 54(4), 86–96 (2011). https://doi.org/10.1145/1924421.1924442

    Article  Google Scholar 

  11. Ekanayake, J., Li, H., Zhang, B., Gunarathne, T., Bae, S.H., Qiu, J., Fox, G.: Twister: a runtime for iterative mapreduce. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC’10, pp. 810–818. ACM, New York, NY, USA (2010). https://doi.org/10.1145/1851476.1851593

  12. George, L.: HBase: The Definitive Guide. Definitive Guide Series. O’Reilly Media, Incorporated (2011). https://books.google.gr/books?id=Ytbs4fLHDakC

  13. Ghemawat, S., Gobioff, H., Leung, S.T.: The Google file system. SIGOPS Oper. Syst. Rev. 37(5), 29–43 (2003). https://doi.org/10.1145/1165389.945450

    Article  Google Scholar 

  14. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Ullah Khan, S.: The rise of big data on cloud computing. Inf. Syst. 47(C), 98–115 (2015). https://doi.org/10.1016/j.is.2014.07.006

    Article  Google Scholar 

  15. Hoffman, S.: Apache Flume: Distributed Log Collection for Hadoop. Community Experience Distilled, 2nd edn. Packt Publishing, Birmingham (2015). https://books.google.gr/books?id=u1bTBgAAQBAJ

  16. Hunt, P., Konar, M., Junqueira, F.P., Reed, B.: Zookeeper: wait-free coordination for internet-scale systems. In: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference, USENIXATC’10, pp. 11–11. USENIX Association, Berkeley, CA, USA (2010). http://dl.acm.org/citation.cfm?id=1855840.1855851

  17. Iosup, A., Yigitbasi, N., Epema, D.: On the performance variability of production cloud services. In: 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 104–113 (2011). https://doi.org/10.1109/CCGrid.2011.22

  18. Peters, B.: The age of big data (2012). https://www.forbes.com/sites/bradpeters/2012/07/12/the-age-of-big-data

  19. Lakshman, A., Malik, P.: The Apache Cassandra project (2011)

    Google Scholar 

  20. Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 2nd edn. Cambridge University Press, New York (2014)

    Book  Google Scholar 

  21. Manyika, J., Chui, M., Institute, M.G., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey, Lexington (2011). https://books.google.gr/books?id=vN1CYAAACAAJ

  22. Noguchi, Y.: The search for analysts to make sense of big data (2011). http://www.npr.org/2011/11/30/142893065/the-searchforanalyststo-make-sense-of-big-data

  23. Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig Latin: a not-so-foreign language for data processing. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD’08, pp. 1099–1110. ACM, New York, NY, USA (2008). https://doi.org/10.1145/1376616.1376726

  24. Owen, S., Anil, R., Dunning, T., Friedman, E.: Mahout in Action. Manning Publications Company, Greenwich (2011)

    Google Scholar 

  25. Patterson, D.A.: Technical perspective: the data center is the computer. Commun. ACM 51(1), 105–105 (2008). https://doi.org/10.1145/1327452.1327491

    Article  Google Scholar 

  26. Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, 1st edn, pp. 229–248. AAAI Press /The MIT Press, Menlo Park, California (1991)

    Google Scholar 

  27. Rabkin, A., Katz, R.: Chukwa: a system for reliable large-scale log collection. In: Proceedings of the 24th International Conference on Large Installation System Administration, LISA’10, pp. 1–15. USENIX Association, Berkeley, CA, USA (2010). http://dl.acm.org/citation.cfm?id=1924976.1924994

  28. Sarstedt, M., Mooi, E.: Cluster Analysis, pp. 273–324. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-53965-7_9

    Google Scholar 

  29. Schad, J., Dittrich, J., Quiané-Ruiz, J.A.: Runtime measurements in the cloud: observing, analyzing, and reducing variance. Proc. VLDB Endow. 3(1–2), 460–471 (2010). https://doi.org/10.14778/1920841.1920902

    Article  Google Scholar 

  30. Sheng, G., Hou, H., Jiang, X., Chen, Y.: A novel association rule mining method of big data for power transformers state parameters based on probabilistic graph model. IEEE Trans. Smart Grid 9(2), 695–702 (2018). https://doi.org/10.1109/TSG.2016.2562123

    Article  Google Scholar 

  31. Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endow. 2(2), 1626–1629 (2009). https://doi.org/10.14778/1687553.1687609

    Article  Google Scholar 

  32. Upadhyaya, S.R.: Parallel approaches to machine learning-a comprehensive survey. J. Parallel Distrib. Comput. 73(3), 284–292 (2013). https://doi.org/10.1016/j.jpdc.2012.11.001

    Article  Google Scholar 

  33. Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014). https://doi.org/10.1109/TKDE.2013.109

    Article  Google Scholar 

  34. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud’10, pp. 10–10. USENIX Association, Berkeley, CA, USA (2010). http://dl.acm.org/citation.cfm?id=1863103.1863113

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Authors and Affiliations

  1. School of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Nikolaos Karagiorgos & Kostas Siozios

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  1. Nikolaos Karagiorgos
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  2. Kostas Siozios
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Correspondence to Kostas Siozios .

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Editors and Affiliations

  1. Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Kostas Siozios

  2. Department of Computer Science, National Technical University of Athens, Athens, Greece

    Dimitrios Anagnostos

  3. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Dimitrios Soudris

  4. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Elias Kosmatopoulos

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Karagiorgos, N., Siozios, K. (2019). Data Analytic for Improving Operations and Maintenance in Smart-Grid Environment. In: Siozios, K., Anagnostos, D., Soudris, D., Kosmatopoulos, E. (eds) IoT for Smart Grids. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-03640-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-03640-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03169-5

  • Online ISBN: 978-3-030-03640-9

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