Advertisement

Cluster Computing

, Volume 22, Supplement 1, pp 1065–1077 | Cite as

Collaborative data analytics for smart buildings: opportunities and models

  • Sanja Lazarova-MolnarEmail author
  • Nader Mohamed
Article
  • 313 Downloads

Abstract

Smart buildings equipped with state-of-the-art sensors and meters are becoming more common. Large quantities of data are being collected by these devices. For a single building to benefit from its own collected data, it will need to wait for a long time to collect sufficient data to build accurate models to help improve the smart buildings systems. Therefore, multiple buildings need to cooperate to amplify the benefits from the collected data and speed up the model building processes. Apparently, this is not so trivial and there are associated challenges. In this paper, we study the importance of collaborative data analytics for smart buildings, its benefits, as well as presently possible models of carrying it out. Furthermore, we present a framework for collaborative fault detection and diagnosis as a case of collaborative data analytics for smart buildings. We also provide a preliminary analysis of the energy efficiency benefit of such collaborative framework for smart buildings. The result shows that significant energy savings can be achieved for smart buildings using collaborative data analytics.

Keywords

Smart buildings Collaborative data analytics Models Energy efficiency Fault detection and diagnosis 

Notes

Acknowledgements

Funding was provided by Innovation Fund Denmark (Grant No. 4106-00003B).

References

  1. 1.
    Agarwal, Y., Gupta, R., Komaki, D., Weng, T.: Buildingdepot: an extensible and distributed architecture for building data storage, access and sharing. In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pp. 64–71. ACM (2012)Google Scholar
  2. 2.
    Amasyali, K., El-Gohary, N.: Building lighting energy consumption prediction for supporting energy data analytics. Procedia Eng. 145, 511–517 (2016)CrossRefGoogle Scholar
  3. 3.
    Balac, N., Sipes, T., Wolter, N., Nunes, K., Sinkovits, B., Karimabadi, H.: Large scale predictive analytics for real-time energy management. In: 2013 IEEE International Conference on Big Data, pp. 657–664. IEEE (2013)Google Scholar
  4. 4.
    Commission, E.: Energy performance of buildings: commission refers Spain to court. http://europa.eu/rapid/press-release_IP-11-1447_en.htm. Accessed 10 Oct 2017 (2011)
  5. 5.
    Diong, B., Zheng, G., Ginn, M.: Establishing the foundation for energy management on university campuses via data analytics. In: SoutheastCon 2015, pp. 1–7. IEEE (2015)Google Scholar
  6. 6.
    Frenklach, M., Packard, A., Seiler, P., Feeley, R.: Collaborative data processing in developing predictive models of complex reaction systems. Int. J. Chem. Kinet. 36(1), 57–66 (2004)CrossRefGoogle Scholar
  7. 7.
    Granzer, W., Praus, F., Kastner, W.: Security in building automation systems. IEEE Trans. Ind. Electron. 57(11), 3622–3630 (2010)CrossRefGoogle Scholar
  8. 8.
    Hong, T., Yang, L., Hill, D., Feng, W.: Data and analytics to inform energy retrofit of high performance buildings. Appl. Energy 126, 90–106 (2014)CrossRefGoogle Scholar
  9. 9.
    Jarrah Nezhad, A., Wijaya, T.K., Vasirani, M., Aberer, K.: Smartd: Smart meter data analytics dashboard. In: Proceedings of the 5th International Conference on Future Energy Systems, pp. 213–214. ACM (2014)Google Scholar
  10. 10.
    Kotsiantis, S., Kanellopoulos, D.: Association rules mining: a recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)Google Scholar
  11. 11.
    Krioukov, A., Goebel, C., Alspaugh, S., Chen, Y., Culler, D.E., Katz, R.H.: Integrating renewable energy using data analytics systems: challenges and opportunities. IEEE Data Eng. Bull. 34(1), 3–11 (2011)Google Scholar
  12. 12.
    Kwac, J., Rajagopal, R.: Demand response targeting using big data analytics. In: 2013 IEEE International Conference on Big Data, pp. 683–690. IEEE (2013)Google Scholar
  13. 13.
    Lazarova-Molnar, S., Kjrgaard, M.B., Shaker, H.R., Jrgensen, B.N.: Commercial buildings energy performance within context: occupants in spotlight. In: SmartGreens (2015)Google Scholar
  14. 14.
    Lazarova-Molnar, S., Logason, H.R., Andersen, P.G., Kjrgaard, M.B.: Mobile crowdsourcing of data for fault detection and diagnosis in smart buildings. In: 2016 ACM Research in Adaptive and Convergent Systems (2016)Google Scholar
  15. 15.
    Lazarova-Molnar, S., Mohamed, N.: Towards collaborative data analytics for smart buildings. In: International Conference of Information Science and Applications (ICISA) 2017, LNEE. Springer (2017).  https://doi.org/10.1007/978-981-10-0557-2_90
  16. 16.
    Lazarova-Molnar, S., Shaker, H.R., Mohamed, N.: Fault detection and diagnosis for smart buildings: state of the art, trends and challenges. In: 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–7. IEEE (2016)Google Scholar
  17. 17.
    Lazarova-Molnar, S., Shaker, H.R., Mohamed, N.: Reliability of cyber physical systems with focus on building management systems. In: 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), pp. 1–6. IEEE (2016)Google Scholar
  18. 18.
    Liu, X., Golab, L., Ilyas, I.F.: Smas: A smart meter data analytics system. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 1476–1479. IEEE (2015)Google Scholar
  19. 19.
    Mohamed, N., Al-Jaroodi, J., Lazarova-Molnar, S.: Energy cloud: services for smart buildings. In: Sustainable Cloud and Energy Services, pp. 117–134. Springer, Berlin (2018)Google Scholar
  20. 20.
    Mohamed, N., Lazarova-Molnar, S., Al-Jaroodi, J.: CE-BEMS: A cloud-enabled building energy management system. In: 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–6. IEEE (2016)Google Scholar
  21. 21.
    Mohamed, N., Lazarova-Molnar, S., Al-Jaroodi, J.: SBDaaS: Smart building diagnostics as a service on the cloud. In: 2016 2nd International Conference on Intelligent Green Building and Smart Grid (IGBSG), pp. 1–6. IEEE (2016)Google Scholar
  22. 22.
    Nikolaou, T.G., Kolokotsa, D.S., Stavrakakis, G.S., Skias, I.D.: On the application of clustering techniques for office buildings’ energy and thermal comfort classification. IEEE Trans. Smart Grid 3(4), 2196–2210 (2012)CrossRefGoogle Scholar
  23. 23.
    Nol, S., Lemire, D.: On the challenges of collaborative data processing. Collaborative Information Behavior: User Engagement and Communication Sharing: User Engagement and Communication Sharing, p. 55 (2010)Google Scholar
  24. 24.
    Prasad, S., Avinash, S.: Smart meter data analytics using opentsdb and hadoop. In: Innovative Smart Grid Technologies-Asia (ISGT Asia), 2013 IEEE, pp. 1–6. IEEE (2013)Google Scholar
  25. 25.
    Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., Pavlou, C., Doukas, P., Primikiri, E., Geros, V.: Using intelligent clustering techniques to classify the energy performance of school buildings. Energy Build. 39(1), 45–51 (2007)CrossRefGoogle Scholar
  26. 26.
    Seiler, P., Frenklach, M., Packard, A., Feeley, R.: Numerical approaches for collaborative data processing. Optim. Eng. 7(4), 459–478 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Simmhan, Y., Aman, S., Kumbhare, A., Liu, R., Stevens, S., Zhou, Q., Prasanna, V.: Cloud-based software platform for big data analytics in smart grids. Comput. Sci. Eng. 15(4), 38–47 (2013)CrossRefGoogle Scholar
  28. 28.
    Singh, A., Bansal, V.: Energy data analytics towards energy-efficient operations for industrial and commercial consumers. In: International Conference on Big Data Analytics, pp. 165–168. Springer (2014)Google Scholar
  29. 29.
    Singh, R.P., Keshav, S., Brecht, T.: A cloud-based consumer-centric architecture for energy data analytics. In: Proceedings of the Fourth International Conference on Future Energy Systems, pp. 63–74. ACM (2013)Google Scholar
  30. 30.
    Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Acm Sigmod Record, vol. 25, pp. 1–12. ACM (1996)Google Scholar
  31. 31.
    Venayagamoorthy, G.K., Rohrig, K., Erlich, I.: One step ahead: short-term wind power forecasting and intelligent predictive control based on data analytics. IEEE Power Energy Mag. 10(5), 70–78 (2012)CrossRefGoogle Scholar
  32. 32.
    Wang, S., Xie, J.: Integrating building management system and facilities management on the internet. Autom. Constr. 11(6), 707–715 (2002)CrossRefGoogle Scholar
  33. 33.
    Wilkinson, D.M., Huberman, B.A.: Cooperation and Quality in Wikipedia. pp. 157–164. ACM (2007)Google Scholar
  34. 34.
    Zeifman, M.: Smart meter data analytics: Prediction of enrollment in residential energy efficiency programs. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 413–416. IEEE (2014)Google Scholar
  35. 35.
    Zhou, K., Yang, S.: Understanding household energy consumption behavior: the contribution of energy big data analytics. Renew. Sustain. Energy Rev. 56, 810–819 (2016)CrossRefGoogle Scholar
  36. 36.
    Zucker, G., Habib, U., Blöchle, M., Wendt, A., Schaat, S., Siafara, L.C.: Building energy management and data analytics. In: 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST), pp. 462–467. IEEE (2015)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.University of Southern DenmarkOdenseDenmark
  2. 2.Middleware Technologies LabPittsburghUSA

Personalised recommendations