A Review on Application of Machine Learning in Building Performance Prediction

R. W. Triadji, M. A. Berawi, M. Sari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Designers usually use Building Performance Simulation (BPS) to support decision making in facing design requirements and expected building performance. However, the fact is that BPS still experiences several limitations, such as BPS requires high computation time in assessing various design options. Machine learning is considered capable of solving the problem that the existing BPS has. Research on this problem has been conducted to provide solutions and prove the reliability of machine learning in predicting building performance. Therefore, this paper aims to discuss the research and overview of how machine learning has been used in predicting building performance. The results show that, performance prediction using machine learning has been developed on energy and environmental performance. Also, machine learning can significantly reduce the prediction time without reducing its accuracy.

Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering - ICRMCE 2021
EditorsStefanus Adi Kristiawan, Buntara S. Gan, Mohamed Shahin, Akanshu Sharma
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-9
Number of pages7
ISBN (Print)9789811693472
DOIs
Publication statusPublished - 2023
Event5th International Conference on Rehabilitation and Maintenance in Civil Engineering, ICRMCE 2021 - Virtual, Online
Duration: 8 Jul 20219 Jul 2021

Publication series

NameLecture Notes in Civil Engineering
Volume225
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference5th International Conference on Rehabilitation and Maintenance in Civil Engineering, ICRMCE 2021
CityVirtual, Online
Period8/07/219/07/21

Keywords

  • Building performance
  • Energy performance
  • Environmental performance
  • Machine learning

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