TY - JOUR

T1 - Early-stage design support combining machine learning and building information modelling

AU - Singh, Manav Mahan

AU - Deb, Chirag

AU - Geyer, Philipp

N1 - Funding Information: The authors want to acknowledge the support of Deutsche Forschungsgemeinschaft (DFG), Germany, for funding the research through the grant GE1652/3-1 within the research unit FOR 2363. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI, Belgium. The authors are thankful to Academische Stichting Leuven for providing support for a research visit to ETH Zürich. We are grateful to researchers from ETH, Zürich, TU München, KU Leuven, Chalmers University of Technology, Sweden, and architects from ARUP Berlin and freelance architects working in Belgium and India who have provided their valuable feedback on the tool. We would like to express our sincere gratitude to the Institute of Energy Efficient and Sustainable Design and Building, Technical University, Munich (TUM) and Ferdinand Tausendpfund GmbH & Co. KG for providing energy consumption and design data for the Tausendpfund building.

PY - 2022/4

Y1 - 2022/4

N2 - 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.

AB - 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.

KW - BIM

KW - Building performance simulation

KW - Design space exploration

KW - Early design stage

KW - Energy efficiency

KW - ML energy predictions

KW - Sensitivity analysis

UR - http://www.scopus.com/inward/record.url?scp=85123824421&partnerID=8YFLogxK

U2 - 10.1016/j.autcon.2022.104147

DO - 10.1016/j.autcon.2022.104147

M3 - Article

AN - SCOPUS:85123824421

VL - 136

JO - Automation in construction

JF - Automation in construction

SN - 0926-5805

M1 - 104147

ER -