TY - GEN
T1 - A Review on Application of Machine Learning in Building Performance Prediction
AU - Triadji, R. W.
AU - Berawi, M. A.
AU - Sari, M.
N1 - Funding Information:
The author is grateful to Universitas Indonesia for giving support in this research.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Building performance
KW - Energy performance
KW - Environmental performance
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85137751131&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-9348-9_1
DO - 10.1007/978-981-16-9348-9_1
M3 - Conference contribution
AN - SCOPUS:85137751131
SN - 9789811693472
T3 - Lecture Notes in Civil Engineering
SP - 3
EP - 9
BT - Proceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering - ICRMCE 2021
A2 - Kristiawan, Stefanus Adi
A2 - Gan, Buntara S.
A2 - Shahin, Mohamed
A2 - Sharma, Akanshu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Rehabilitation and Maintenance in Civil Engineering, ICRMCE 2021
Y2 - 8 July 2021 through 9 July 2021
ER -