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Component-Based Machine Learning for Energy Performance Prediction by MultiLOD Models in the Early Phases of Building Design

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Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10863))

Abstract

The application of building information modeling (BIM) in early design phases requires the support of different levels of detail (LOD). This allows scaling to be supported as an important activity of designing. Furthermore, to achieve well-performing solutions in terms of energy efficiency, it is necessary to consider energy performance in early design stages. Therefore, this paper presents a multiLOD modeling approach for the early phases of building design that integrates energy performance prediction based on component-based machine learning (ML) using artificial neural networks (ANN). A model structure with three adaptive LOD definitions is proposed to support the design process by a digital model that supports flexible scaling back and forth. By linking the ML models to the elements in this structure, components are formed that support quick and flexible modeling and energy performance prediction in the early building design process. The transformation rules flexibly link the ML components to all LOD. This approach was illustrated and validated by a test case with a medium-sized office building. The early design states of the case were reconstructed for the application of the method. For validation purposes, the results of the ML predictions for 60 different design configurations were compared to those of a conventional parametric full-detail simulation model. This comparison showed that the average error was no higher than 3.8% for heating and 3.5% for cooling.

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References

  1. Eastman, C., Teicholz, P., Sacks, R., Liston, K.: BIM Handbook. Wiley, Hoboken (2011)

    Google Scholar 

  2. Borrmann, A., König, M., Koch, C., Beetz, J.: Building Information Modeling - Technologische Grundlagen und industrielle Praxis. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-658-05606-3

    Book  Google Scholar 

  3. Sawhney, A., Maheswari, J.U.: Design Coordination Using Cloud-based Smart Building Element Models. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 5, 445–453 (2013)

    Google Scholar 

  4. BuildingSMART: Industry Foundation Classes IFC2x4 (2013). http://www.buildingsmart-tech.org/specifications/ifc-releases/ifc4-release/

  5. König, M., Borrmann, A., Geyer, P., Schneider, P., Lang, W., Petzold, F., Schellenbach-Held, M.: Evaluation of building design variants in early phases on the basis of adaptive detailing strategies and system-based simulation of energy flows for such models. In: Presented at the Beyond BIM Workshop, Ghent (2017)

    Google Scholar 

  6. Kim, H., Anderson, K.: Energy modeling system using building information modeling open standards. J. Comput. Civ. Engi. 27, 203–211 (2013)

    Article  Google Scholar 

  7. Singaravel, S., Geyer, P., Suykens, J.: Component-based Machine Learning Modelling Approach For Design Stage Building Energy Prediction. In: IBPSA 2017 (2017)

    Google Scholar 

  8. Geyer, P., Singaravel, S.: Component-based building performance prediction using systems engineering and machine learning. Appl. Energy (2018, submitted)

    Google Scholar 

  9. Schlueter, A., Thesseling, F.: Building information model based energy/exergy performance assessment in early design stages. Autom. Constr. 18, 153–163 (2009)

    Article  Google Scholar 

  10. Clarke, J., Hensen, J.: Integrated building performance simulation: progress, prospects and requirements. Build. Environ. 91, 294–306 (2015)

    Article  Google Scholar 

  11. Gero, J.S., Kumar, B.: Expanding design spaces through new design variables. Des. Stud. 14, 210–221 (1993)

    Article  Google Scholar 

  12. Gane, V., Haymaker, J.: Design scenarios: enabling transparent parametric design spaces. Adv. Eng. Inform. 26, 618–640 (2012)

    Article  Google Scholar 

  13. Østergård, T., Jensen, R.L., Maagaard, S.E.: Building simulations supporting decision making in early design – a review. Renew. Sustain. Energy Rev. 61, 187–201 (2016)

    Article  Google Scholar 

  14. van Treeck, C., Rank, E.: Dimensional reduction of 3D building models using graph theory and its application in building energy simulation. Eng. Comput. 23, 109–122 (2007)

    Article  Google Scholar 

  15. Ahn, K.U., Kim, Y.J., Park, C.S., Kim, I., Lee, K.: BIM interface for full vs. semi-automated building energy simulation. Energy Build. 68, 671–678 (2014)

    Article  Google Scholar 

  16. Gervásio, H., Santos, P., Martins, R., da Silva, L.S.: A macro-component approach for the assessment of building sustainability in early stages of design. Build. Environ. 73, 256–270 (2014)

    Article  Google Scholar 

  17. Dogan, T., Reinhart, C., Michalatos, P.: Autozoner: an algorithm for automatic thermal zoning of buildings with unknown interior space definitions. J. Build. Perform. Simul. 9, 176–189 (2016)

    Article  Google Scholar 

  18. Granadeiro, V., Duarte, J.P., Palensky, P.: Building envelope shape design using a shape grammar-based parametric design system integrating energy simulation. In: IEEE Africon 2011, pp. 1–6. IEEE, Livingstone (2011)

    Google Scholar 

  19. ASHRAE: Standard 90.1–2013. Energy Standard for Buildings Except Low-Rise Residential Buildings, Atlanta, GA (2013)

    Google Scholar 

  20. Stiny, G.: Shape: Talking About Seeing and Doing. MIT Press, Cambridge (2006)

    Google Scholar 

  21. Mitchell, W.J., Liggett, R.S., Pollalis, S.N., Tan, M.: Integrating shape grammars and design analysis. In: Computer Aided Architectural Design Futures: Education, Research, Applications, proceedings of CAAD Futures 1991. pp. 17–32. CAAD Futures, Zürich (1991)

    Google Scholar 

  22. Geyer, P.: Multidisciplinary grammars supporting design optimization of buildings. Res. Eng. Des. 18, 197–216 (2008)

    Article  Google Scholar 

  23. Schmidt, L.C., Shetty, H., Chase, S.C.: A graph grammar approach for structure synthesis of mechanisms. J. Mech. Des. 122, 371–376 (2000)

    Article  Google Scholar 

  24. Helms, B., Shea, K.: Computational synthesis of product architectures based on object-oriented graph grammars. J. Mech. Des. 134, 021008-14 (2012)

    Article  Google Scholar 

  25. BIMForum: Level of Development Specification (2016)

    Google Scholar 

  26. NATSPEC Construction Information: NATSPEC National BIM Guide (2011)

    Google Scholar 

  27. National Institute of Building Sciences buildingSMART alliance: National BIM Standard - United States TM Version 2 (2015)

    Google Scholar 

  28. National Building Specifications: NBS BIM Toolkit. https://toolkit.thenbs.com/support

  29. Building and Construction Authority: Singapore BIM Guide - Version 2.0., Singapore (2013)

    Google Scholar 

  30. NATSPEC BIM - NATSPEC National BIM Guide. https://bim.natspec.org/documents/natspec-national-bim-guide

  31. Yaneva, A.: Scaling up and down: extraction trials in architectural design. Soc. Stud. Sci. 35, 867–894 (2005)

    Article  Google Scholar 

  32. Ammon, S.: Why designing is not experimenting: design methods, epistemic praxis and strategies of knowledge acquisition in architecture. Philos. Technol. 30, 495–520 (2017)

    Article  Google Scholar 

  33. Borrmann, A., Kolbe, T., Donaubauer, A., Steuer, H., Jubierre, J.R.: Transferring multi-scale approaches from 3D city modeling to IFC-based tunnel modeling. In: Proceedings of the 3DGeoInfo, pp. 27–29 (2013)

    Article  Google Scholar 

  34. Borrmann, A., Jubierre, J.R.: A multi-scale tunnel product model providing coherent geometry and semantics. In: Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering, pp. 1–8 (2013)

    Google Scholar 

  35. Meng, L., Forberg, A.: 3D building generalisation. Gen. Geogr. Inf. Cartogr. Model. Appl. Elsevier. (2007)

    Chapter  Google Scholar 

  36. Glander, T., Döllner, J.: Abstract representations for interactive visualization of virtual 3D city models. Comput. Environ. Urban Syst. 33, 375–387 (2009)

    Article  Google Scholar 

  37. Eisenhower, B., O’Neill, Z., Narayanan, S., Fonoberov, V.: A methodology for meta-model based optimization in building energy models. Energy Build. 47, 292–301 (2012)

    Article  Google Scholar 

  38. Magoulès, F., Zhao, H.: Data Mining and Machine Learning in Building Energy Analysis. Wiley Online Library (2016)

    Book  Google Scholar 

  39. Van Gelder, L., Das, P., Janssen, H., Roels, S.: Comparative study of metamodelling techniques in building energy simulation: guidelines for practitioners. Simul. Model. Pract. Theory 49, 245–257 (2014)

    Article  Google Scholar 

  40. Stavrakakis, G., Zervas, P., Sarimveis, H., Markatos, N.: Optimization of window-openings design for thermal comfort in naturally ventilated buildings. Appl. Math. Model. 36, 193–211 (2012)

    Article  Google Scholar 

  41. Cheng, M.-Y., Cao, M.-T.: Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines. Appl. Soft Comput. 22, 178–188 (2014)

    Article  Google Scholar 

  42. Object Management Group: Systems Modeling Language, Specifications Version 1.5. http://www.omg.org/spec/SysML/1.5/

  43. Singaravel, S., Geyer, P., Suykens, J.: Deep neural network architectures for component-based machine learning model in building energy predictions. Presented at the eg-ice Workshop 2017 (2017)

    Google Scholar 

  44. Singaravel, S., Geyer, P., Suykens, J.: Deep learning neural networks architectures and methods: building design energy prediction by component-based models. Adv. Eng. Inform. (2018, submitted)

    Google Scholar 

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Acknowledgements

The research presented in this paper was funded by a KU Leuven starting grant StG/14/020 and by the German Research Foundation (DFG) in the Researcher Unit 2363 “Evaluation of building design variants in early phases using adaptive levels of detail” in Subproject 4 “System-based Simulation of Energy Flows”. The multiLOD approach was developed in discussion with the members of the research group (DFG Researcher Unit).

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Authors and Affiliations

  1. KU Leuven, Kasteelpark Arenberg 1 - box 2431, 3001, Leuven, Belgium

    Philipp Geyer, Manav Mahan Singh & Sundaravelpandian Singaravel

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  1. Philipp Geyer
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  2. Manav Mahan Singh
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  3. Sundaravelpandian Singaravel
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Corresponding author

Correspondence to Philipp Geyer .

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Editors and Affiliations

  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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Geyer, P., Singh, M.M., Singaravel, S. (2018). Component-Based Machine Learning for Energy Performance Prediction by MultiLOD Models in the Early Phases of Building Design. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10863. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-91635-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91634-7

  • Online ISBN: 978-3-319-91635-4

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