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

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Autorschaft

  • Manav Mahan Singh
  • Sundaravelpandian Singaravel
  • Philipp Florian Geyer

Externe Organisationen

  • KU Leuven
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksLife-Cycle Analysis and Assessment in Civil Engineering
UntertitelTowards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018
Herausgeber/-innenDan M. Frangopol, Robby Caspeele, Luc Taerwe
ErscheinungsortGhent
Seiten487-494
Seitenumfang8
PublikationsstatusVeröffentlicht - 2019
Extern publiziertJa
Veranstaltung6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018 - Ghent, Belgien
Dauer: 28 Okt. 2018 → 31 Okt. 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 Sachgebiete

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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. Hrsg. / Dan M. Frangopol; Robby Caspeele; Luc Taerwe. Ghent, 2019. S. 487-494.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › 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 (Hrsg.), 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, S. 487-494, 6th International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, Ghent, Belgien, 28 Okt. 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 (Hrsg.), 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 (S. 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, Hrsg., 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. S. 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. Hrsg. / Dan M. Frangopol ; Robby Caspeele ; Luc Taerwe. Ghent, 2019. S. 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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