Building Multi-occupancy Analysis and Visualization Through Data Intensive Processing

  • Dimosthenis Ioannidis
  • Pantelis Tropios
  • Stelios KrinidisEmail author
  • Dimitris Tzovaras
  • Spiridon Likothanassis
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 475)


A novel Building Multi-occupancy Analysis & Visualization through Data Intensive Processing techniques is going to be presented in this paper. Building occupancy monitoring plays an important role in increasing energy efficiency and provides useful semantic information about the usage of different spaces and building performance generally. In this paper the occupancy extraction subsystem is constituted by a collection of depth image cameras and a multi-sensorial cloud (utilizing big data from various sensor types) in order to extract the occupancy per space. Furthermore, a number of novel visual analytics techniques allow the end-users to process big data in different temporal resolutions in a compact and comprehensive way taking into account properties of human cognition and perception, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed building occupancy analysis system has been tested and applied to various spaces of CERTH premises with different characteristics in a real-life testbed environment.


Big data analysis Building occupancy Occupancy extraction Human presence Building occupancy visualization 



This work has been partially supported by the European Commission through the project HORIZON 2020-RESEARCH & INNOVATION ACTIONS (RIA)-696129-GREENSOUL.


  1. 1.
    Meyn, S., et. al.: A sensor-utility-network method for estimation of occupancy in buildings. In: 48th IEEE Conference on Decision and Control 2009, pp. 1494–1500 (2009)Google Scholar
  2. 2.
    Dodier, R.H., Henze, G.P., Tiller, D.K., Guo, X.: Building occupancy detection through sensor belief networks. Energy Build. 38(9), 1033–1043 (2006)CrossRefGoogle Scholar
  3. 3.
    Yang, Z., et al.: The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use. Energy Build. (2014)Google Scholar
  4. 4.
    Lam, K.P., et al.: Information-theoretic environmental features selection for occupancy detection in open offices. In: 11th International IBPSA Conference (2009)Google Scholar
  5. 5.
    Hoes, P., Hensen, J.L.M., Loomans, M.G.L.C., de Vries, B., Bourgeois, D.: User behavior in whole building simulation. Energy Build. (2009)Google Scholar
  6. 6.
    Page, J., Robinson, D., Morel, N., Scartezzini, J.-L.: A generalised stochastic model for the simulation of occupant presence. Energy Build. 40(2), 83–98 (2008)CrossRefGoogle Scholar
  7. 7.
    Duarte, C., Van Den Wymelenberg, K., Rieger, C.: Revealing occupancy patterns in an office building through the use of occupancy sensor data. Energy Build. (2013)Google Scholar
  8. 8.
    Menezes, A.C., et al.: Predicted vs. actual energy performance of non-domestic buildings: using post-occupancy evaluation data to reduce the performance gap. Appl. Energy (2012)Google Scholar
  9. 9.
    (Annie) Egan, A.M.: Occupancy of Australian office buildings: how accurate are typical assumptions used in energy performance simulation and what is the impact of inaccuracy. ASHRAE Trans. 118(1), 217–224 (2012)Google Scholar
  10. 10.
    Eguaras-Martinez, M., et al.: Simulation and evaluation of building information modeling in a real pilot site. Appl. Energy 114, 475–484 (2014)CrossRefGoogle Scholar
  11. 11.
    Caucheteux, A., et al.: Occupancy measurement in building: a literature review, application on an energy efficiency research demonstrated building. Int. J. Metrol. (2013)Google Scholar
  12. 12.
    Erickson, V.L., Carreira-Perpiñán, M.Á., Cerpa, A.E.: Occupancy modeling and prediction for building energy management. ACM Trans. Sens. Networks 10(3), 1–28 (2014)CrossRefGoogle Scholar
  13. 13.
    Ke, M., et al.: Analysis of building energy consumption parameters and energy savings measurement and verification by applying eQUEST software. Energy Build. 61, 100–107 (2013)CrossRefGoogle Scholar
  14. 14.
    Dong, B., et al.: An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy Build. (2010)Google Scholar
  15. 15.
    Kuutti, J., et al.: Real Time Building Zone Occupancy Detection and Activity Visualization Utilizing a Visitor Counting Sensor Network, February 2014Google Scholar
  16. 16.
    Mahdavi, A.: Patterns and implications of user control actions in buildings. Indoor Built Environ. 18(5), 440–446 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Erickson, V.L., et al.: Occupancy based demand response HVAC control strategy. In: Proceedings of the 2nd ACM Workshop on Embedded Systems for EE in Building (2010)Google Scholar
  18. 18.
    Tabak, V.: User Simulation of Space Utilisation (2009)Google Scholar
  19. 19.
    Krinidis, S., et al.: A Robust and real-time multi-space occupancy extraction system exploiting privacy-preserving sensors. In: 6th International Symposium on Communications, Control and Signal Processing (ISCCSP 2014) (2014)Google Scholar
  20. 20.
    Kintzel, C., Fuchs, J., Mansmann, F.: Monitoring large IP spaces with clock-view. In: 8th International Symposium on Visualization for Cyber Security, ACM, New York (2011)Google Scholar
  21. 21.
    Stellmach, S., Nache, L., Dachselt, T.: 3D attentional maps: aggregated gaze visualizations in three dimensional virtual environments. In: Proceedings of the International Conference on Advanced Visual Interfaces, pp. 345–348. ACM, New York (2010)Google Scholar
  22. 22.
    Nguyen, T.A., Aiello, M.: Energy intelligent buildings based on user activity: a survey. Energy Build. 56, 244–257 (2013)., ISSN 0378-7788CrossRefGoogle Scholar
  23. 23.
    Agarwal, Y., Balaji, B., Gupta, R., Lyles, J., Wei, M., Weng, T.: Occupancy-driven energy management for smart building automation. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys 2010, pp. 1–6. ACM, New York (2010)Google Scholar
  24. 24.
    Nguyen, T.A., Aiello, M.: Beyond indoor presence monitoring with simple sensors. In: Proceedings of the 2nd International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS), pp. 5–14 (2012)Google Scholar
  25. 25.
    Hailemariam, E., et al.: Real-time occupancy detection using decision trees with multiple sensor types. In: Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design. Society for Computer Simulation International (2011)Google Scholar
  26. 26.
    Yang, Z., et al.: A systematic approach to occupancy modelling in ambient sensor–rich buildings. Simulation (2013). doi: 10.1177/0037549713489918
  27. 27.
    Kuutti, J., Saarikko, P., Sepponen, R.E.: Real time building zone occupancy detection and activity visualization utilizing a visitor counting sensor network. In: 11th International Conference on Remote Engineering and Virtual Instrumentation (REV), IEEE, Polytechnic of Porto (ISEP) in Porto, Portugal, 26-28 February, 2014, pp. 219-224 (2014)Google Scholar
  28. 28.
    Ekwevugbe, T., Brown, N., Fan, D.: A design model for building occupancy detection using sensor fusion. In: 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST), Campione d’Italia, 18-20 June 2012, pp. 1–6 (2012)Google Scholar
  29. 29.
    Wahl, F., Milenkovic, M., Amft, O.: A distributed PIR-based approach for estimating people count in office environments. In: Proceedings of the IEEE 15th International Conference on Computational Science and Engineering (CSE 2012), IEEE, Washington, DC (2012)Google Scholar
  30. 30.
    Zikos, S., et al.: Conditional Random fields-based approach for real-time building occupancy estimation with multi-sensory networks. Autom. Constr. 68 (2016)Google Scholar
  31. 31.
    Fanger, P.O.: Analysis and Applications in Environmental Engineering. McGraw-Hill Book Company, New York (1970)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Dimosthenis Ioannidis
    • 1
    • 2
  • Pantelis Tropios
    • 1
  • Stelios Krinidis
    • 1
    Email author
  • Dimitris Tzovaras
    • 1
  • Spiridon Likothanassis
    • 2
  1. 1.Information Technologies Institute, Centre for Research and Technology HellasThermi-ThessalonikiGreece
  2. 2.Computer Engineering and InformaticsUniversity of PatrasRio, PatrasGreece

Personalised recommendations