Early-stage design support combining machine learning and building information modelling

verfasst von

Manav Mahan Singh, Chirag Deb, Philipp Geyer

Abstract

Global energy concerns necessitate designing energy-efficient buildings. Many important decisions affecting energy performance are made at early stages with little information. Dynamic simulations support informed decision-making; however, uncertainty, high computational time, and expensive modelling efforts impair their use at early stages. This article develops an approach using building information modelling and machine learning that provides quick energy performance information. This approach has been implemented into a web tool, p-energyanalysis.de. It allows design space exploration, assesses the energy performance of design options, compares multiple options, performs sensitivity analysis, and tracks changes. Twenty-one participants (researchers and architects) used it as a support tool for designing an energy-efficient building. Their feedbacks are discussed as part of the tool development. The study found that the tool supports early-stage design decisions by quickly providing relevant information. The limitations, such as the bias in the results towards training data population and implementation issues, are also discussed.

Organisationseinheit(en)

Institut für Entwerfen und Konstruieren

Externe Organisation(en)

KU Leuven
Indian Institute of Technology Bombay (IITB)
ETH Zürich

Typ

Artikel

Journal

Automation in construction

Band

136

ISSN

0926-5805

Publikationsdatum

04.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Tief- und Ingenieurbau, Bauwesen

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.1016/j.autcon.2022.104147 (Zugang: Geschlossen)