Machine learning-based model predictive control for multizone building automation: A case study

Abstract

In Singapore’s hot and humid climate, air-conditioning and mechanical ventilation (ACMV) systems account for over 60% of commercial building energy consumption, driving efforts to enhance energy efficiency through predictive control strategies such as model predictive control (MPC) to overcome the limitations of conventional reactive building automation systems. This paper presents a multizone MPC system designed to optimize energy consumption and thermal comfort in a commercial building’s ACMV system in Singapore. The system was implemented in a multi-use test building with real occupancy and a deployment area of approximately 850 m², partitioned into six learning zones, two office spaces, and three open spaces. The ACMV system serving the deployment area consisted of two primary air-handling units and 16 fan coil units, where chilled water was supplied to the cooling coils, and conditioned air was distributed through motorized diffusers. To facilitate predictive control, data-driven thermal prediction models were developed for each zone using a non-linear autoregressive exogenous network with exogenous inputs trained on historical data and disturbances. Thermal comfort optimization was guided by the predictive mean vote, which was targeted at 0, representing thermal neutrality (as per ASHRAE 55 standards), and constrained within a range of −0.5 - 0.5. Performance comparisons demonstrated that the MPC system achieved over 42% energy savings compared to the original thermostat-based control while enhancing thermal comfort. Despite its advantageous control performances, challenges for large-scale deployment remain, including implementation costs, scalability, and model accuracy. Future work can address these challenges by developing comfort models that leverage existing building sensors.

Keywords

Model predictive control / Coordinated multisystem control / Energy saving / Thermal comfort / Visual comfort / High-performance building

Acknowledgments

Technical support from Dr. Wai Soong Loh from JTC for this project is much appreciated. We also appreciate the patience, understanding, and logistical support of the administrative team from Civil Service College, Singapore, for this project.

Funding

This research is jointly supported by Jurong Town Corporation (JTC) of Singapore (NTU REF 2019-0607) and Smart Nation and Digital Government Office, SNDGO of Singapore (NRF2016IDM-TRANS001-031).

Conflict of interest

The authors declare they have no competing interests.

Author contributions

Conceptualization: Pradeep Shakya, Man Pun Wan

Data Curation: Shiva Sreenivasan

Formal analysis: Pradeep Shakya, Man Pun Wan

Funding acquisition: Shiyu Yang, Man Pun Wan

Investigation: Shiva Sreenivasan, Pradeep Shakya

Methodology: Pradeep Shakya

Resources: Baskaran Krishnamoorthy, Shiyu Yang

Writing - original draft: Pradeep Shakya, Shiva Sreenivasan

Writing - review & editing: Man Pun Wan, Pradeep Shakya, Shiva Sreenivasan

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

The datasets presented in this article are not readily available due to a confidentiality agreement with the funding agency.

Further disclosure

Part of or the entire set of findings have been presented at 2024 Herrick Conferences in Compressor Engineering, Refrigeration and Air Conditioning and High-Performance Buildings (held on July 14, 2024, at Purdue University, West Lafayette, Indiana, United States of America) hosted by the Ray W. Herrick Laboratories and the Center for High-Performance Buildings in West Lafayette, Indiana, United States of America.

References

[1] Building and Construction Authority (BCA). Super Low Energy Building Technology Roadmap. BAvailable from: https://www1.bca.gov.sg/docs/default-source/docs-corp-buildsg/sustainability/sle-tech-roadmap-report--published-ver1-1.pdf?sfvrsn=f2df22ed_0 [Last accessed on 2024 Dec 05].

[2] Singapore Green Building Council (SGBC). Singapore Green Building Masterplan. Singapore Green Building Council; 2020. Available from: https://www.sgbc.sg/about-green-building/sgbmp [Last accessed on 2024 Dec 05].

[3] Building and Construction Authority (BCA). Green Building Masterplan. Building and Construction Authority; 2021. Available from: https://www1.bca.gov.sg/docs/default-source/docs-corp-buildsg/sustainability/sgbmp-80-80-80-in-2030-infographic.pdf?sfvrsn=57172d48_2 [Last accessed on 2024 Dec 05].

[4] Das HP, Lin YW, Agwan U, et al. Machine learning for smart and energy-efficient buildings. Environ Data Sci. 2024;3:e1. doi: 10.1017/eds.2023.43

[5] Qiang G, Tang S, Hao J, Di Sarno L, Wu G, Ren S. Building automation systems for energy and comfort management in green buildings: A critical review and future directions. Renew Sustain Energy Rev. 2023;179:113301. doi: 10.1016/j.rser.2023.113301

[6] Pinheiro S, Wimmer R, O’Donnell J, et al. MVD based information exchange between BIM and building energy performance simulation. Autom Constr. 2018; 90:91-103. doi: 10.1016/j.autcon.2018.02.009

[7] Mauri L, Vallati A, Ocłoń P. Low impact energy saving strategies for individual heating systems in a modern residential building: A case study in Rome. J Clean Prod. 2019; 214:791-802. doi: 10.1016/j.jclepro.2018.12.320

[8] Mathews Roy A, Prasanna Venkatesan R, Shanmugapriya T. Simulation and analysis of a factory building’s energy consumption using eQuest software. Chem Eng Technol. 2021; 44(5):928-933. doi: 10.1002/ceat.202000489

[9] Andriamamonjy A, Saelens D, Klein R. An automated IFC-based workflow for building energy performance simulation with Modelica. Autom Constr. 2018; 91:166-181. doi: 10.1016/j.autcon.2018.03.019

[10] Yang S, Wan MP, Ng BF, et al. A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings. Energy Build. 2018; 170:25-39. doi: 10.1016/j.enbuild.2018.03.082

[11] Yang S, Wan MP. Machine-learning-based model predictive control with instantaneous linearization-A case study on an air-conditioning and mechanical ventilation system. Appl Energy. 2022;306:118041. doi: 10.1016/j.apenergy.2021.118041

[12] Široký J, Oldewurtel F, Cigler J, Prívara S. Experimental analysis of model predictive control for an energy efficient building heating system. Appl Energy. 2011; 88:3079-3087. doi: 10.1016/j.apenergy.2011.03.009

[13] Ma Y, Borrelli F, Hencey B, Coffey B, Bengea S, Haves P. Model predictive control for the operation of building cooling systems. IEEE Trans Control Syst Technol. 2012; 20:796-803. doi: 10.1109/TCST.2011.2124461

[14] Pang X, Duarte C, Haves P, Chuang F. Testing and demonstration of model predictive control applied to a radiant slab cooling system in a building test facility. Energy Build. 2018; 172:432-441. doi: 10.1016/j.enbuild.2018.05.013

[15] Yang Y, Srinivasan S, Hu G, Spanos CJ. Distributed control of multizone HVAC systems considering indoor air quality. IEEE Trans Control Syst Technol. 2021; 29:2586-2597. doi: 10.1109/TCST.2020.3047407

[16] Yang S, Chen W, Wan MP. A machine-learning-based event-triggered model predictive control for building energy management. Build Environ. 2023;233:110101. doi: 10.1016/j.buildenv.2023.110101

[17] Fathi S, Srinivasan R, Fenner A, Fathi S. Machine learning applications in urban building energy performance forecasting: A systematic review. Renew Sustain Energy Rev. 2020;133:110287. doi: 10.1016/j.rser.2020.110287

[18] Seyedzadeh S, Rahimian FP, Rastogi P, Glesk I. Tuning machine learning models for prediction of building energy loads. Sustain Cities Soc. 2019;47:101484. doi: 10.1016/j.scs.2019.101484

[19] Zhou X, Xu L, Zhang J, et al. Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database. Energy Build. 2020;211:109795. doi: 10.1016/j.enbuild.2020.109795

[20] Chaudhuri T, Zhai D, Soh YC, Li H, Xie L. Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology. Energy Build. 2018; 166:391-406. doi: 10.1016/j.enbuild.2018.02.035

[21] Koschwitz D, Frisch J, Van Treeck C. Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale. Energy. 2018; 165:134-142. doi: 10.1016/j.energy.2018.09.068

[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 prediction combined with an interpretation process. Energy Build. 2020;225:110301. doi: 10.1016/j.enbuild.2020.110301

[23] Beltran A and Cerpa AE. Optimal HVAC Building Control with Occupancy Prediction. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-efficient Buildings; 2014. p. 168-171. doi: 10.1145/2674061.2674072

[24] Li B, Xia L. A Multi-grid Reinforcement Learning Method for Energy Conservation and Comfort of HVAC in Buildings. In: 2015 IEEE International Conference on Automation Science and Engineering (CASE); 2015. p. 444-449. doi: 10.1109/CoASE.2015.7294119

[25] Zhang Z and Lam KP. Practical Implementation and Evaluation of Deep Reinforcement Learning Control for a Radiant Heating System. In: Proceedings of the 5^th Conference on Systems for Built Environments; 2018. p. 148-157. doi: 10.1145/3276774.3276775

[26] Ding X, Cerpa A, Du W. Exploring deep reinforcement learning for holistic smart building control. ACM Trans Sens Netw. 2024; 20(3):1-28. doi: 10.1145/3656043

[27] Shamachurn H, Seebaruth M, Kowlessur NS, Hassen SS. Real‐time model predictive control of air‐conditioners through IoT-results from an experimental setup in a tropical climate. Adv Control Appl Eng Ind Syst. 2024;6:e232. doi: 10.1002/adc2.232

[28] Hu G, You F. Multi-zone building control with thermal comfort constraints under disjunctive uncertainty using data-driven robust model predictive control. Adv Appl Energy. 2023;9:100124. doi: 10.1016/j.adapen.2023.100124

[29] Joe J, Im P, Cui B, Dong J. Model-based predictive control of multi-zone commercial building with a lumped building modelling approach. Energy. 2023;263:125494. doi: 10.1016/j.energy.2022.125494

[30] Oldewurtel F, Parisio A, Jones CN, et al. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Build. 2012; 45:15-27. doi: 10.1016/j.enbuild.2011.09.022

[31] Hou J, Li H, Nord N, Huang G. Model predictive control under weather forecast uncertainty for HVAC systems in university buildings. Energy Build. 2022;257:111793. doi: 10.1016/j.enbuild.2021.111793

[32] Mazar MM, Rezaeizadeh A. Adaptive model predictive climate control of multi-unit buildings using weather forecast data. J Build Eng. 2020;32:101449. doi: 10.1016/j.jobe.2020.101449

[33] ASHRAE Handbook. Heating, Ventilating, and Air- Conditioning Systems and Equipment. Vol. 39. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air- Conditioning Engineers, Inc.; 1996.

[34] Meteorological Service Singapore (MSS). Climate of Singapore. Meteorological Service Singapore; 2020. Available from : https://www.weather.gov.sg/climate-climate-of-singapore [Last accessed on 2024 Dec 05].

[35] Yang S, Wan MP, Ng BF, et al. Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems. Appl Energy. 2021;297:117112. doi: 10.1016/j.apenergy.2021.117112

[36] Freedman DA. Statistical Models: Theory and Practice. United Kingdom: Cambridge University Press; 2009.

[37] Chifu VR, Pop CB, Chifu ES, Barleanu H. Deep Learning for Forecasting the Energy Consumption in Public Buildings. In: 2021 20^th RoEduNet Conference: Networking in Education and Research (RoEduNet); 2021. p. 1-6. doi: 10.48550/arXiv.2207.11953

[38] Yang R, Hao J, Jiang H, Jin X. Machine-Learning-Driven, 2020, Site-Specific Weather Forecasting for Grid-Interactive Efficient Buildings. Golden, CO, United States: National Renewable Energy Lab. (NREL); 2020. Available from: https://www.osti.gov/biblio/1669587 [Last accessed on 2025 Feb 06].

[39] Yang S, Wan MP, Chen W, Ng BF, Dubey S. Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Appl Energy. 2020;271:115147. doi: 10.1016/j.apenergy.2020.115147

[40] Zhao S, Cajo R, De Keyser R, Liu S, Ionescu CM. Nonlinear predictive control applied to steam/water loop in large scale ships. IFAC PapersOnLine. 2019; 52(1):868-873. doi: 10.1016/j.ifacol.2019.06.171