Component-based machine learning for energy performance prediction by multiLOD models in the early phases of building design

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Authors

  • Philipp Florian Geyer
  • Manav Mahan Singh
  • Sundaravelpandian Singaravel

External Research Organisations

  • KU Leuven
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Details

Original languageEnglish
Title of host publicationAdvanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings
EditorsBernd Domer, Ian F. Smith
PublisherSpringer Verlag
Pages516-534
Number of pages19
ISBN (electronic)978-3-319-91635-4
ISBN (print)978-3-319-91634-7
Publication statusPublished - 19 May 2018
Externally publishedYes
Event25th Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2018 - Lausanne, Switzerland
Duration: 10 Jun 2018 → 13 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer, Cham
Volume10863
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

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.

Keywords

    Building information modeling (BIM), Component-based machine learning, Early design phase, Multi-level-of-detail modeling

ASJC Scopus subject areas

Sustainable Development Goals

Cite this

Component-based machine learning for energy performance prediction by multiLOD models in the early phases of building design. / Geyer, Philipp Florian; Singh, Manav Mahan; Singaravel, Sundaravelpandian.
Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. ed. / Bernd Domer; Ian F. Smith. Springer Verlag, 2018. p. 516-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10863).

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Geyer, PF, Singh, MM & Singaravel, S 2018, Component-based machine learning for energy performance prediction by multiLOD models in the early phases of building design. in B Domer & IF Smith (eds), Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10863, Springer Verlag, pp. 516-534, 25th Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2018, Lausanne, Switzerland, 10 Jun 2018. https://doi.org/10.1007/978-3-319-91635-4_27
Geyer, P. F., 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 B. Domer, & I. F. Smith (Eds.), Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings (pp. 516-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10863). Springer Verlag. https://doi.org/10.1007/978-3-319-91635-4_27
Geyer PF, Singh MM, Singaravel S. Component-based machine learning for energy performance prediction by multiLOD models in the early phases of building design. In Domer B, Smith IF, editors, Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. Springer Verlag. 2018. p. 516-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-91635-4_27
Geyer, Philipp Florian ; Singh, Manav Mahan ; Singaravel, Sundaravelpandian. / Component-based machine learning for energy performance prediction by multiLOD models in the early phases of building design. Advanced Computing Strategies for Engineering - 25th EG-ICE International Workshop 2018, Proceedings. editor / Bernd Domer ; Ian F. Smith. Springer Verlag, 2018. pp. 516-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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