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

verfasst von

Manav Mahan Singh, Sundaravelpandian Singaravel, Philipp Florian Geyer

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.

Externe Organisation(en)

KU Leuven

Typ

Aufsatz in Konferenzband

Seiten

487-494

Anzahl der Seiten

8

Publikationsdatum

2019

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Tief- und Ingenieurbau, Sicherheit, Risiko, Zuverlässigkeit und Qualität

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie