Page 57 - IJAMD-2-1

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

doi: 10.1016/j.scs.2019.101484 29. Joe J, Im P, Cui B, Dong J. Model-based predictive control
of multi-zone commercial building with a lumped building
19. Zhou X, Xu L, Zhang J, et al. Data-driven thermal comfort
model via support vector machine algorithms: Insights from modelling approach. Energy. 2023;263:125494.
ASHRAE RP-884 database. Energy Build. 2020;211:109795. doi: 10.1016/j.energy.2022.125494
doi: 10.1016/j.enbuild.2020.109795 30. Oldewurtel F, Parisio A, Jones CN, et al. Use of model
20. Chaudhuri T, Zhai D, Soh YC, Li H, Xie L. Random forest predictive control and weather forecasts for energy efficient
based thermal comfort prediction from gender-specific building climate control. Energy Build. 2012;45:15-27.
physiological parameters using wearable sensing technology. doi: 10.1016/j.enbuild.2011.09.022
Energy Build. 2018;166:391-406.
31. Hou J, Li H, Nord N, Huang G. Model predictive control
doi: 10.1016/j.enbuild.2018.02.035 under weather forecast uncertainty for HVAC systems in
21. Koschwitz D, Frisch J, Van Treeck C. Data-driven heating university buildings. Energy Build. 2022;257:111793.
and cooling load predictions for non-residential buildings doi: 10.1016/j.enbuild.2021.111793
based on support vector machine regression and NARX
Recurrent Neural Network: A comparative study on district 32. Mazar MM, Rezaeizadeh A. Adaptive model predictive
scale. Energy. 2018;165:134-142. climate control of multi-unit buildings using weather
forecast data. J Build Eng. 2020;32:101449.
doi: 10.1016/j.energy.2018.09.068
doi: 10.1016/j.jobe.2020.101449
22. Zhang C, Li J, Zhao Y, Li T, Chen Q, Zhang X. A hybrid deep
learning-based method for short-term building energy load 33. ASHRAE Handbook. Heating, Ventilating, and Air-
prediction combined with an interpretation process. Energy Conditioning Systems and Equipment. Vol. 39. Atlanta, GA,
Build. 2020;225:110301. USA: American Society of Heating, Refrigerating and Air-
Conditioning Engineers, Inc.; 1996.
doi: 10.1016/j.enbuild.2020.110301
34. Meteorological Service Singapore (MSS). Climate of Singapore.
23. Beltran A and Cerpa AE. Optimal HVAC Building Control Meteorological Service Singapore; 2020. Available from :
with Occupancy Prediction. In: Proceedings of the 1 ACM https://www.weather.gov.sg/climate-climate-of-singapore
st
Conference on Embedded Systems for Energy-efficient [Last accessed on 2024 Dec 05].
Buildings; 2014. p. 168-171.
35. Yang S, Wan MP, Ng BF, et al. Model predictive control for
doi: 10.1145/2674061.2674072 integrated control of air-conditioning and mechanical ventilation,
24. Li B, Xia L. A Multi-grid Reinforcement Learning Method for lighting and shading systems. Appl Energy. 2021;297:117112.
Energy Conservation and Comfort of HVAC in Buildings. doi: 10.1016/j.apenergy.2021.117112
In: 2015 IEEE International Conference on Automation
Science and Engineering (CASE); 2015. p. 444-449. 36. Freedman DA. Statistical Models: Theory and Practice.
United Kingdom: Cambridge University Press; 2009.
doi: 10.1109/CoASE.2015.7294119
37. Chifu VR, Pop CB, Chifu ES, Barleanu H. Deep Learning for
25. Zhang Z and Lam KP. Practical Implementation and Forecasting the Energy Consumption in Public Buildings.
Evaluation of Deep Reinforcement Learning Control for a In: 2021 20 RoEduNet Conference: Networking in Education
th
Radiant Heating System. In: Proceedings of the 5 Conference and Research (RoEduNet); 2021. p. 1-6.
th
on Systems for Built Environments; 2018. p. 148-157.
doi: 10.48550/arXiv.2207.11953
doi: 10.1145/3276774.3276775
38. Yang R, Hao J, Jiang H, Jin X. Machine-Learning-Driven, 2020,
26. Ding X, Cerpa A, Du W. Exploring deep reinforcement Site-Specific Weather Forecasting for Grid-Interactive Efficient
learning for holistic smart building control. ACM Trans Sens Buildings. Golden, CO, United States: National Renewable
Netw. 2024;20(3):1-28. Energy Lab. (NREL); 2020. Available from: https://www.osti.
doi: 10.1145/3656043 gov/biblio/1669587 [Last accessed on 2025 Feb 06].
27. Shamachurn H, Seebaruth M, Kowlessur NS, Hassen SS. 39. Yang S, Wan MP, Chen W, Ng BF, Dubey S. Model predictive
Real‐time model predictive control of air‐conditioners control with adaptive machine-learning-based model for
through IoT-results from an experimental setup in a tropical building energy efficiency and comfort optimization. Appl
climate. Adv Control Appl Eng Ind Syst. 2024;6:e232. Energy. 2020;271:115147.
doi: 10.1002/adc2.232 doi: 10.1016/j.apenergy.2020.115147
28. Hu G, You F. Multi-zone building control with thermal comfort 40. Zhao S, Cajo R, De Keyser R, Liu S, Ionescu CM. Nonlinear
constraints under disjunctive uncertainty using data-driven predictive control applied to steam/water loop in large scale
robust model predictive control. Adv Appl Energy. 2023;9:100124. ships. IFAC PapersOnLine. 2019;52(1):868-873.
doi: 10.1016/j.adapen.2023.100124 doi: 10.1016/j.ifacol.2019.06.171

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