Information exchange scenarios between machine learning energy prediction model and BIM at early stage of design

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

Authors

  • Manav Mahan Singh
  • Sundaravelpandian Singaravel
  • Philipp Florian Geyer

External Research Organisations

  • KU Leuven
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Details

Original languageEnglish
Title of host publicationLife-Cycle Analysis and Assessment in Civil Engineering
Subtitle of host publicationTowards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018
EditorsDan M. Frangopol, Robby Caspeele, Luc Taerwe
Place of PublicationGhent
Pages487-494
Number of pages8
Publication statusPublished - 2019
Externally publishedYes
Event6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018 - Ghent, Belgium
Duration: 28 Oct 2018 → 31 Oct 2018

Abstract

The building design process incorporates various analysis activities for design space exploration. The need of sustainable built-facility has made energy efficiency an important factor through building lifecycle. Building information modelling (BIM) facilitates energy analysis by reducing re-modelling efforts to create energy model. However, the lack of information makes energy prediction a challenging task in the early design phase with a deterministic approach. The research work analyses various information exchange scenarios at different levels of detail (LOD) that link to an approach of machine learning energy prediction model with BIM data. At any level of detail, information is distinguished by the labels “available”, “developing” and “unknown”. Monte Carlo method will be used to generate samples of energy analysis for unknown information. The uncertainty of energy prediction is represented by mean, maximum and minimum values of heating load. The research will be useful for design space exploration at the early stage of design.

ASJC Scopus subject areas

Sustainable Development Goals

Cite this

Information exchange scenarios between machine learning energy prediction model and BIM at early stage of design. / Singh, Manav Mahan; Singaravel, Sundaravelpandian; Geyer, Philipp Florian.
Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. ed. / Dan M. Frangopol; Robby Caspeele; Luc Taerwe. Ghent, 2019. p. 487-494.

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

Singh, MM, Singaravel, S & Geyer, PF 2019, Information exchange scenarios between machine learning energy prediction model and BIM at early stage of design. in DM Frangopol, R Caspeele & L Taerwe (eds), Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. Ghent, pp. 487-494, 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, Ghent, Belgium, 28 Oct 2018.
Singh, M. M., Singaravel, S., & Geyer, P. F. (2019). Information exchange scenarios between machine learning energy prediction model and BIM at early stage of design. In D. M. Frangopol, R. Caspeele, & L. Taerwe (Eds.), Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018 (pp. 487-494).
Singh MM, Singaravel S, Geyer PF. Information exchange scenarios between machine learning energy prediction model and BIM at early stage of design. In Frangopol DM, Caspeele R, Taerwe L, editors, Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. Ghent. 2019. p. 487-494
Singh, Manav Mahan ; Singaravel, Sundaravelpandian ; Geyer, Philipp Florian. / Information exchange scenarios between machine learning energy prediction model and BIM at early stage of design. Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018. editor / Dan M. Frangopol ; Robby Caspeele ; Luc Taerwe. Ghent, 2019. pp. 487-494
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abstract = "The building design process incorporates various analysis activities for design space exploration. The need of sustainable built-facility has made energy efficiency an important factor through building lifecycle. Building information modelling (BIM) facilitates energy analysis by reducing re-modelling efforts to create energy model. However, the lack of information makes energy prediction a challenging task in the early design phase with a deterministic approach. The research work analyses various information exchange scenarios at different levels of detail (LOD) that link to an approach of machine learning energy prediction model with BIM data. At any level of detail, information is distinguished by the labels “available”, “developing” and “unknown”. Monte Carlo method will be used to generate samples of energy analysis for unknown information. The uncertainty of energy prediction is represented by mean, maximum and minimum values of heating load. The research will be useful for design space exploration at the early stage of design.",
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Download

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