Advertisement

Use Cases for Improved Analysis of Energy and Comfort Related Parameters Based on BIM and BEMS Data

  • Filip PetrushevskiEmail author
  • Maryam Montazer
  • Stefan Seifried
  • Christian Schiefer
  • Gerhard Zucker
  • Thomas Preindl
  • Georg Suter
  • Wolfgang Kastner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10864)

Abstract

A facility passes through several life cycle phases: conceptual design, design development, construction, use, reuse, remodeling, and demolition. In each phase, documents are created by architects, engineers, technical planners, and contractors that reflect the facility’s state at certain points in time. Information exchange is achieved with commercial or open data exchange standards. Using building information modeling (BIM), complex three-dimensional and semantically rich building models are feasible that facilitate planning and data exchange in project teams and through the whole lifecycle. Such models have significant potential not only for design and construction, but also to improve building operation. This paper investigates how BIM may be applied to improve operational efficiency in facilities. More specifically, the aim is to achieve improved reporting and visualization of energy and comfort related parameters, as well as their engineering and commissioning, by application of BIM in combination with building energy management systems (BEMS). We present use cases that will guide the development of a novel dynamic BIM concept in which facility data are combined with building management system data. As a conclusion, an analysis of the feasibility of the use cases in terms of information availability is provided.

Keywords

BIM BEMS Energy Comfort 

Notes

Acknowledgment

This work was funded under the project “Building Information Modeling for Building Energy Management Systems” (BIM4BEMS) by the FFG (Austrian Research Promotion Agency) program City of Tomorrow (project number 854677).

References

  1. 1.
    International Energy Agency: Energy Technology Perspectives 2016 - Towards Sustainable Urban Energy Systems. OECD/IEA (2016)Google Scholar
  2. 2.
    International Energy Agency: Tracking Clean Energy Progress 2017. OECD/IEA (2017)Google Scholar
  3. 3.
    Hurtado, L., Nguyen, P., Kling, W., Zeiler, W.: Building energy management systems—Optimization of comfort and energy use. In: 2013 48th International Universities’ Power Engineering Conference (UPEC) (2013)Google Scholar
  4. 4.
    Virk, G.S., Alkadhimi, K.I.H., Cheung, J.M., Loveday, D.L.: Advanced control techniques for BEMS. In: Rao, R.B.K.N., Hope, A.D. (eds.) COMADEM 89 International: Proceedings of the First International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM), pp. 463–468. Springer, Boston (1989).  https://doi.org/10.1007/978-1-4684-8905-7_74CrossRefGoogle Scholar
  5. 5.
    Manic, M., Wijayasekara, D., Amarasinghe, K., Rodriguez-Andina, J.J.: Building energy management systems: the age of intelligent and adaptive buildings. IEEE Ind. Electron. Mag. 10(1), 25–39 (2016)CrossRefGoogle Scholar
  6. 6.
    Schachinger, D., Gaida, S., Kastner, W., Petrushevski, F., Reinthaler, C., Sipetic, M., Zucker, G.: An advanced data analytics framework for energy efficiency in buildings. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) (2016)Google Scholar
  7. 7.
    Sipetic, M., Petrushevski, F., Judex, F.: Comparison of machine learning algorithms for forecasting of residential complex energy consumption. In: 24th Workshop of the European Group for Intelligent Computing in Engineering, Nottingham, UK (2017)Google Scholar
  8. 8.
    Molina-Solana, M., Ros, M., Ruiz, M.D., Gómez-Romero, J., Martin-Bautista, M.: Data science for building energy management: a review. Renew. Sustain. Energy Rev. 70, 598–609 (2017)CrossRefGoogle Scholar
  9. 9.
    Navigant Research: Research report: Market Data: Building Energy Management Systems, September 2017. https://www.navigantresearch.com/research/market-data-building-energy-management-systems. Accessed Jan 2018
  10. 10.
    Ock, J., Issa, R.R., Flood, I.: Smart building energy management systems (BEMS) simulation conceptual framework. In: Winter Simulation Conference (WSC) 2016 (2016)Google Scholar
  11. 11.
    Dong, B., O’Neill, Z., Li, Z.: A BIM-enabled information infrastructure for building energy fault detection and diagnostics. Autom. Constr. 44, 197–211 (2014)CrossRefGoogle Scholar
  12. 12.
    McGlinn, K., Yuce, B., Wicaksono, H., Howell, S., Rezgui, Y.: Usability evaluation of a web-based tool for supporting holistic building energy management. Autom. Constr. 84, 154–165 (2017)CrossRefGoogle Scholar
  13. 13.
    Minoli, D., Sohraby, K., Occhiogrosso, B.: IoT considerations, requirements, and architectures for smart buildings—energy optimization and next-generation building management systems. IEEE Internet Things J. 4(1), 269–283 (2017)Google Scholar
  14. 14.
    Kazmi, A.H., O’grady, M.J., Delaney, D.T., Ruzzelli, A.G., O’hare, G.M.: A review of wireless-sensor-network-enabled building energy management systems. ACM Trans. Sens. Netw. (TOSN) 10(4), 66 (2014)Google Scholar
  15. 15.
    Coates, A., Hammoudeh, M., Holmes, K.G.: Internet of things for buildings monitoring: experiences and challenges. In: Proceedings of the International Conference on Future Networks and Distributed Systems (2017)Google Scholar
  16. 16.
    IBM: Watson Internet of Things (2018). https://www.ibm.com/internet-of-things. Accessed Jan 2018
  17. 17.
    Microsoft: Microsoft Azure (2018). https://azure.microsoft.com. Accessed Jan 2018
  18. 18.
    Siemens: MindSphere (2018). https://siemens.mindsphere.io/. Accessed Jan 2018
  19. 19.
    Honeywell: Honeywell Building Solutions (2018). https://buildingsolutions.honeywell.com. Accessed Jan 2018
  20. 20.
    Johnson Controls: Johnson Controls Building Management (2018). http://www.johnsoncontrols.com/buildings/building-management. Accessed Jan 2018
  21. 21.
    Pacific Controls: Pacific Controls Integrated Building Automation (2018). http://www.pacificcontrols.net/solutions/integrated-building-automation.html. Accessed Jan 2018
  22. 22.
    Schneider Electric: Schneider Electric Smart Building Solutions (2018). https://www.schneider-electric.com/en/work/solutions/system/s1/buildings-systems.jsp. Accessed Jan 2018
  23. 23.
  24. 24.
    Building IQ (2018). https://buildingiq.com/. Accessed Jan 2018
  25. 25.
    Nantum: Prescriptive Data (2018). http://www.prescriptivedata.io/. Accessed Jan 2018
  26. 26.
    SkyFoundry: SkySpark (2018). https://skyfoundry.com/skyspark/. Accessed Jan 2018
  27. 27.
    Eastman, C., Teicholz, P., Sacks, R., Liston, K.: BIM Handbook, A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers, and Contractors. Wiley, Hoboken (2008)Google Scholar
  28. 28.
    Howard, R., Björk, B.: Building information modelling–Experts’ views on standardisation and industry deployment. Adv. Eng. Inform. 22, 271–280 (2008)CrossRefGoogle Scholar
  29. 29.
    ISO 16739: Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries, “International Organization for Standardization” (2013). https://www.iso.org/standard/51622.html. Accessed Jan 2018
  30. 30.
    Green Building XML (gbXML) Schema, Inc.: Open Green Building XML Schema: a Building Information Modeling solution for our green world (2018). http://www.gbxml.org. Accessed Jan 2018
  31. 31.
    Model support group of BuildingSMART Alliance: Industry foundation classes, IFC4 official release (2013). http://www.buildingsmart-tech.org/ifc/IFC4/final/html/index.htm. Accessed Jan 2018
  32. 32.
    Wang, H., Gluhak, A., Meissner, S., Tafazoli, H.: Integration of BIM and live sensing information to monitor building energy performance. In: The CIB 30th Intternational Conference on Application of IT in AEC Industry, Beijing, China (2013)Google Scholar
  33. 33.
    Dong, B., Lam, K.P., Huang, Y., Dobbs, G.M.: A comparative study of the IFC and gbXML informational infrastructures for data exchange in computational design support environments Geometry information. In: Building Simulation 2007 (2007)Google Scholar
  34. 34.
    Adachi, Y.: Overview of IFC model server framework. In: eWork and eBusiness in Architecture, Engineering and Construction. CRC Press, Boca Raton (2002)Google Scholar
  35. 35.
    Chipman, T., Liebich, T., Weise, M.: mvdXMl specification 1.1. Model Support Group (MSG) of buildingSMART International Ltd. (2016)Google Scholar
  36. 36.
    Model Support Group of BuildingSMART: Model View Definition (MVD) speciification, buildingSMART International Ltd. (2018). http://www.buildingsmart-tech.org/specifications/ifc-view-definition
  37. 37.
    East, E.: Construction Operations Building information exchange (COBie): Requirements Definition. U.S. Army, Engineer Research and Development Center, Washington, D.C. (2007)Google Scholar
  38. 38.
    National BIM Standard: Information Exchange Standards, Construction Operation Building information exchange (COBie) – Version 2.4, National Institute of Building Sciences buildingSMART alliance, US (2015)Google Scholar
  39. 39.
    IFMA: International Facility Management Association (2018) https://www.ifma.org/about/what-is-facility-management. Accessed Jan 2018
  40. 40.
    ARCHIBUS, Inc.: ARCHIBUS. www.archibus.com. Accessed Jan 2018
  41. 41.
    ProFMSoftware: “ArchiFM,” vintoCON Ltd. http://www.archifm.net/. Accessed Jan 2018
  42. 42.
    Pärn, E., Edwards, D., Sing, M.: The building information modelling trajectory in facilities management: a review. Autom. Constr. 75, 45–55 (2017)CrossRefGoogle Scholar
  43. 43.
    Studer, R., Benjamins, R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25, 161–198 (1998)CrossRefGoogle Scholar
  44. 44.
    Baader, F., Horrocks, I., Sattler, U.: Description logics. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. INFOSYS, pp. 3–28. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24750-0_1CrossRefGoogle Scholar
  45. 45.
    Schachinger, D., Kastner, W.: Semantics for smart control of building automation. In: Proceedings of the IEEE 25th International Symposium on Industrial Electronics (ISIE) (2016)Google Scholar
  46. 46.
    Sure, Y., Staab, S., Studer, R.: Ontology engineering methodology. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 135–152. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-92673-3_6CrossRefzbMATHGoogle Scholar
  47. 47.
    Bonino, D., Corno, F.: DogOnt - ontology modeling for intelligent domotic environments. In: Sheth, A., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 790–803. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88564-1_51CrossRefGoogle Scholar
  48. 48.
    Kofler, M., Reinisch, C., Kastner, W.: A semantic representation of energy-related information in future smart homes. Energy Build. 47, 169–179 (2012)CrossRefGoogle Scholar
  49. 49.
    Balaji, B., Bhattacharya, A., Fierro, G., Gao, J., Gluck, J., Hong, D., Johansen, A., Koh, J., Ploennigs, J., Agarwal, Y., Berges, M., Culler, D., Gupta, R., Kjaergaard, M.B., Srivastava, M., Whitehouse, K.: Brick: towards a unified metadata schema for buildings. In: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, Palo Alto, CA, USA (2016)Google Scholar
  50. 50.
    Haystack, P.: Project Haystack. https://project-haystack.org. Accessed 11 Jan 2018
  51. 51.
    Volkov, A., Chelyshkov, P., Lysenko, D.: Information management in the application of BIM in construction. The roles and functions of the participants of the construction process. Procedia Eng. 153, 828–832 (2016)CrossRefGoogle Scholar
  52. 52.
    Hove, S.E., Anda, B.: Experiences from conducting semi-structured interviews in empirical software engineering research. In: 11th IEEE International Software Metrics Symposium (METRICS 2005) (2005)Google Scholar
  53. 53.
    Object Management Group: OMG Unified Modeling Language (OMG UML) Version 2.5 Specification (2015). http://www.omg.org/spec/UML/2.5
  54. 54.
    Object Management Group: Business Process Model And Notation BPMN Version 2.0 (2011). http://www.omg.org/spec/BPMN/2.0/

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Filip Petrushevski
    • 1
    Email author
  • Maryam Montazer
    • 2
  • Stefan Seifried
    • 3
  • Christian Schiefer
    • 4
  • Gerhard Zucker
    • 1
  • Thomas Preindl
    • 3
  • Georg Suter
    • 2
  • Wolfgang Kastner
    • 3
  1. 1.AIT Austrian Institute of TechnologyViennaAustria
  2. 2.Design Computing GroupTU WienViennaAustria
  3. 3.Automation Systems GroupTU WienViennaAustria
  4. 4.Caverion AustriaViennaAustria

Personalised recommendations