Advertisement

In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data

  • Ekaterina Petrova
  • Pieter Pauwels
  • Kjeld Svidt
  • Rasmus Lund Jensen
Conference paper

Abstract

Cross-domain analytical techniques have made the prediction of outcomes in building design more accurate. Yet, many decisions are based on rules of thumb and previous experiences, and not on documented evidence. That results in inaccurate predictions and a difference between predicted and actual building performance. This article aims to reduce the occurrence of such errors using a combination of data mining and semantic modelling techniques, by deploying these technologies in a use case, for which sensor data is collected. The results present a semantic building data graph enriched with discovered motifs and association rules in observed properties. We conclude that the combination of semantic modelling and data mining techniques can contribute to creating a repository of building data for design decision support.

Keywords

BIM Semantics Data mining Pattern recognition Knowledge discovery 

Notes

Acknowledgements

The authors would like to thank Dr. Mads Lauridsen and Aalborg Municipality for providing access to the sensor data used to perform the experiment.

References

  1. 1.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–54 (1996)Google Scholar
  2. 2.
    Soibelman, L., Kim, H.: Data preparation process for construction knowledge generation through knowledge discovery in databases. J. Comput. Civil Eng. 16(1), 39–48 (2002)CrossRefGoogle Scholar
  3. 3.
    Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)Google Scholar
  4. 4.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, NY (2006)zbMATHGoogle Scholar
  5. 5.
    Piatetsky-Shapiro, G.: Knowledge discovery in real databases: a report on the IJCAI-89 workshop. AI Mag. 11(5), 68–70 (1991)Google Scholar
  6. 6.
    Han, J.W., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 3rd edn. Morgan Kaufmann, Waltham, US (2012)zbMATHGoogle Scholar
  7. 7.
    Fan, C., Xiao, F., Li, Z., Wang, J.: Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: a review. Energy Build. 159, 296–308 (2018)CrossRefGoogle Scholar
  8. 8.
    Ahmed, A., Korres, N.E., Ploennigs, J., Elhadi, H., Menzel, K.: Mining building performance data for energy-efficient operation. Adv. Eng. Inform. 25, 341–354 (2011)CrossRefGoogle Scholar
  9. 9.
    Wang, Z., Srinivasan, R.S.: A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew. Sustain. Energy Rev. 75, 796–808 (2017)CrossRefGoogle Scholar
  10. 10.
    Zhao, H., Magoulès, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16, 3586–3592 (2012)CrossRefGoogle Scholar
  11. 11.
    D’Oca, S., Hong, T.: A data-mining approach to discover patterns of window opening and closing behavior in offices. Build. Environ. 82, 726–739 (2014)CrossRefGoogle Scholar
  12. 12.
    Zhao, J., Lasternas, B., Lam, K.P., Yun, R., Loftness, V.: Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy Build. 82, 341–355 (2014)CrossRefGoogle Scholar
  13. 13.
    Cheng, Z., Zhao, Q., Wang, F., Chen, Z., Jiang, Y., Li, Y.: Case studies of fault diagnosis and energy saving in buildings using data mining techniques. In: IEEE International Conference on Automation Science and Engineering, pp. 646–651 (2016)Google Scholar
  14. 14.
    Pena, M., Biscarri, F., Guerrero, J.I., Monedero, I., León, C.: Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach. Expert Syst. Appl. 56, 242–255 (2016)CrossRefGoogle Scholar
  15. 15.
    D’Oca, S., Hong, T.: Occupancy schedules learning process through a data mining framework. Energy Build. 88, 395–408 (2015)CrossRefGoogle Scholar
  16. 16.
    Fan, C., Xiao, F., Yan, C.: A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Autom. Constr. 50, 81–90 (2015)CrossRefGoogle Scholar
  17. 17.
    Yu, Z., Fung, B., Haghighat, F.: Extracting knowledge from building-related data—a data mining framework. Build. Simul. 6(2), 207–222 (2013)CrossRefGoogle Scholar
  18. 18.
    Xiao, F., Fan, C.: Data mining in building automation system for improving building operational performance. Energy Build. 75, 109–118 (2014)CrossRefGoogle Scholar
  19. 19.
    Miller, C., Nagy, Z., Schlueter, A.: Automated daily pattern filtering of measured building performance data. Autom. Constr. 49, 1–17 (2015)CrossRefGoogle Scholar
  20. 20.
    Wu, S., Clements-Croome, D.: Understanding the indoor environment through mining sensory data—a case study. Energy Build. 39, 1183–1191 (2007)CrossRefGoogle Scholar
  21. 21.
    Jun, M.A., Cheng, J.C.P.: Selection of target LEED credits based on project information and climatic factors using data mining techniques. Adv. Eng. Inform. 32, 224–236 (2017)CrossRefGoogle Scholar
  22. 22.
    Peng, Y., Lina, J.R., Zhang, J.P., Hu, Z.Z.: A hybrid data mining approach on BIM-based building operation and maintenance. Build. Environ. 126, 483–495 (2017)CrossRefGoogle Scholar
  23. 23.
    Yarmohammadi, S., Pourabolghasem, R., Shirazi, A., Ashuri, B.: A sequential pattern mining approach to extract information from BIM design log files. In: 33rd International Symposium on Automation and Robotics in Construction, pp. 174–181 (2016)Google Scholar
  24. 24.
    Liu, Y., Huang, Y.C., Stouffs, R.: Using a data-driven approach to support the design of energy-efficient buildings. ITCon 20, 80–96 (2015)Google Scholar
  25. 25.
    Mirakhorli, M., Chen, H., Kazman, R.: Mining big data for detecting, extracting and recommending architectural design concepts. In: IEEE/ACM 1st International Workshop on Big Data Software Engineering, pp. 15–18 (2015)Google Scholar
  26. 26.
    Rasmussen, M.H., Pauwels, P., Hviid, C.A., Karlshøj, J.: Proposing a central AEC ontology that allows for domain specific extensions. In: Proceedings of the Joint Conference on Computing in Construction (JC3), pp. 237–244 (2017)Google Scholar
  27. 27.
    Pauwels, P., Zhang, S., Lee, Y.C.: Semantic web technologies in AEC industry: a literature overview. Autom. Constr. 73, 145–165 (2017)CrossRefGoogle Scholar
  28. 28.
    Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. J. Web Semant. 36, 1–22 (2016)CrossRefGoogle Scholar
  29. 29.
    Rodriguez, I., Lauridsen, M., Vasluianu, G., Poulsen, A.N., Mogensen, P.: The Gigantium smart city living lab: a multi-arena LoRa-based testbed. In: 15th International Symposium on Wireless Communication Systems, Lisbon, Portugal (2018) (in press)Google Scholar
  30. 30.
    Fan, C., Xiao, F., Madsen, H., Wang, D.: Temporal knowledge discovery in big BAS data for building energy management. Energy Build. 109, 75–89 (2015)CrossRefGoogle Scholar
  31. 31.
    Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 17, 164–181 (2011)CrossRefGoogle Scholar
  32. 32.
    Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: Proceedings of the 2002 IEEE International Conference on Data Mining. (2002)Google Scholar
  33. 33.
    Weiner, P.: Linear pattern matching algorithms. In: 14th Annual IEEE Symposium on Switching and Automata Theory, pp. 1–11 (1973)Google Scholar
  34. 34.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringAalborg UniversityAalborgDenmark
  2. 2.Department of Architecture and Urban PlanningGhent UniversityGhentBelgium

Personalised recommendations