Page 56 - IJAMD-2-1

International Journal of AI for
Materials and Design
ML-based MPC for multizone BAC

Consent for publication doi: 10.1016/j.jclepro.2018.12.320

Not applicable. 8. Mathews Roy A, Prasanna Venkatesan R, Shanmugapriya T.
Simulation and analysis of a factory building’s energy
Availability of data consumption using eQuest software. Chem Eng Technol.
2021;44(5):928-933.
The datasets presented in this article are not readily available doi: 10.1002/ceat.202000489
due to a confidentiality agreement with the funding agency.
9. Andriamamonjy A, Saelens D, Klein R. An automated
Further disclosure IFC-based workflow for building energy performance
simulation with Modelica. Autom Constr. 2018;91:166-181.
Part of or the entire set of findings have been presented
at 2024 Herrick Conferences in Compressor Engineering, doi: 10.1016/j.autcon.2018.03.019
Refrigeration and Air Conditioning and High-Performance 10. Yang S, Wan MP, Ng BF, et al. A state-space thermal model
Buildings (held on July 14, 2024, at Purdue University, incorporating humidity and thermal comfort for model
West Lafayette, Indiana, United States of America) hosted predictive control in buildings. Energy Build. 2018;170:25-39.
by the Ray W. Herrick Laboratories and the Center for doi: 10.1016/j.enbuild.2018.03.082
High-Performance Buildings in West Lafayette, Indiana,
United States of America. 11. Yang S, Wan MP. Machine-learning-based model predictive
control with instantaneous linearization-A case study on an
References air-conditioning and mechanical ventilation system. Appl
Energy. 2022;306:118041.
1. Building and Construction Authority (BCA). Super doi: 10.1016/j.apenergy.2021.118041
Low Energy Building Technology Roadmap. Building and
Construction Authority; 2018. Available from: https:// 12. Široký J, Oldewurtel F, Cigler J, Prívara, S. Experimental
www1.bca.gov.sg/docs/default-source/docs-corp-buildsg/ analysis of model predictive control for an energy efficient
sustainability/sle-tech-roadmap-report--published-ver1-1. building heating system. Appl Energy. 2011;88:3079-3087.
pdf?sfvrsn=f2df22ed_0 [Last accessed on 2024 Dec 05]. doi: 10.1016/j.apenergy.2011.03.009
2. Singapore Green Building Council (SGBC). Singapore Green 13. Ma Y, Borrelli F, Hencey B, Coffey B, Bengea S, Haves P. Model
Building Masterplan. Singapore Green Building Council; predictive control for the operation of building cooling
2020. Available from: https://www.sgbc.sg/about-green- systems. IEEE Trans Control Syst Technol. 2012;20:796-803.
building/sgbmp [Last accessed on 2024 Dec 05].
doi: 10.1109/TCST.2011.2124461
3. Building and Construction Authority (BCA). Green Building
Masterplan. Building and Construction Authority; 2021. 14. Pang X, Duarte C, Haves P, Chuang F. Testing and
Available from: https://www1.bca.gov.sg/docs/default-source/ demonstration of model predictive control applied to a
docs-corp-buildsg/sustainability/sgbmp-80-80-80-in-2030- radiant slab cooling system in a building test facility. Energy
infographic.pdf?sfvrsn=57172d48_2 [Last accessed on Build. 2018;172:432-441.
2024 Dec 05]. doi: 10.1016/j.enbuild.2018.05.013
4. Das HP, Lin YW, Agwan U, et al. Machine learning for smart 15. Yang Y, Srinivasan S, Hu G, Spanos CJ. Distributed control
and energy-efficient buildings. Environ Data Sci. 2024;3:e1. of multizone HVAC systems considering indoor air quality.
doi: 10.1017/eds.2023.43 IEEE Trans Control Syst Technol. 2021;29:2586-2597.
5. Qiang G, Tang S, Hao J, Di Sarno L, Wu G, Ren S. Building doi: 10.1109/TCST.2020.3047407
automation systems for energy and comfort management 16. Yang S, Chen W, Wan MP. A machine-learning-based event-
in green buildings: A critical review and future directions. triggered model predictive control for building energy
Renew Sustain Energy Rev. 2023;179:113301. management. Build Environ. 2023;233:110101.
doi: 10.1016/j.rser.2023.113301 doi: 10.1016/j.buildenv.2023.110101
6. Pinheiro S, Wimmer R, O’Donnell J, et al. MVD based 17. Fathi S, Srinivasan R, Fenner A, Fathi S. Machine learning
information exchange between BIM and building energy applications in urban building energy performance
performance simulation. Autom Constr. 2018;90:91-103. forecasting: A systematic review. Renew Sustain Energy Rev.
doi: 10.1016/j.autcon.2018.02.009 2020;133:110287.
7. Mauri L, Vallati A, Ocłoń P. Low impact energy saving doi: 10.1016/j.rser.2020.110287
strategies for individual heating systems in a modern 18. Seyedzadeh S, Rahimian FP, Rastogi P, Glesk I. Tuning
residential building: A case study in Rome. J Clean Prod. machine learning models for prediction of building energy
2019;214:791-802. loads. Sustain Cities Soc. 2019;47:101484.

Volume 2 Issue 1 (2025) 50 doi: 10.36922/ijamd.8161