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Sustainability
Volume 18
Issue 1
10.3390/su18010240
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Model Predictive Control for Coupled Indoor Air Quality and Energy Performance Based on Incremental Thermal Preference Learning: Experimental Validation in Office Environments

by
Jiali Liu
1,
Xiaojia Huang
2,*,
Tianchen Nan
2,
Yiqiao Liu
3,
Sijia Gao
1,
Ying Cui
4 and
Song Pan
1
1
Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China
2
China IPPR International Engineering Co., Ltd., Beijing 100089, China
3
China MCC5 Group Corp., Ltd., Chengdu 610063, China
4
Department of Mechanical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(1), 240; https://doi.org/10.3390/su18010240
Submission received: 17 November 2025 / Revised: 16 December 2025 / Accepted: 22 December 2025 / Published: 25 December 2025
(This article belongs to the Section Green Building)
Versions Notes

Abstract

Occupant-Centric Control (OCC) aims to achieve a balance between personalized comfort and energy efficiency; however, current strategies often optimize either thermal comfort or indoor air quality (IAQ) in isolation. This study presents a model predictive control (MPC) framework that integrates incremental learning of individual thermal preferences with IAQ and energy co-optimization in office buildings. An incremental Naive Bayes classifier updates personalized temperature preference bands. Gray-box models, including an RC-network for temperature and a CO2 mass-balance model, provide multi-step forecasts calibrated via genetic algorithm cross-validation. These learned preferences, along with a CO2 limit, are enforced as constraints within the MPC, which minimizes HVAC energy use, supported by a supervisory layer for preventing inefficient operation and allowing manual override. Python–EnergyPlus co-simulations for an open office and a meeting room demonstrate that the framework maintains CO2 concentrations below 1000 ppm and keeps 95% of temperatures within comfort ranges. Compared with baseline control, HVAC energy use decreased by 66% in winter and 56% in summer for the open office and by 44% in winter and 57% in summer for the meeting room. The proposed framework provides a reproducible approach for HVAC control that simultaneously enhances comfort, indoor environmental quality, and energy performance.
Keywords: model predictive control; thermal preference learning; indoor air quality; office buildings; HVAC energy optimization model predictive control; thermal preference learning; indoor air quality; office buildings; HVAC energy optimization

Share and Cite

MDPI and ACS Style

Liu, J.; Huang, X.; Nan, T.; Liu, Y.; Gao, S.; Cui, Y.; Pan, S. Model Predictive Control for Coupled Indoor Air Quality and Energy Performance Based on Incremental Thermal Preference Learning: Experimental Validation in Office Environments. Sustainability 2026, 18, 240. https://doi.org/10.3390/su18010240

AMA Style

Liu J, Huang X, Nan T, Liu Y, Gao S, Cui Y, Pan S. Model Predictive Control for Coupled Indoor Air Quality and Energy Performance Based on Incremental Thermal Preference Learning: Experimental Validation in Office Environments. Sustainability. 2026; 18(1):240. https://doi.org/10.3390/su18010240

Chicago/Turabian Style

Liu, Jiali, Xiaojia Huang, Tianchen Nan, Yiqiao Liu, Sijia Gao, Ying Cui, and Song Pan. 2026. "Model Predictive Control for Coupled Indoor Air Quality and Energy Performance Based on Incremental Thermal Preference Learning: Experimental Validation in Office Environments" Sustainability 18, no. 1: 240. https://doi.org/10.3390/su18010240

APA Style

Liu, J., Huang, X., Nan, T., Liu, Y., Gao, S., Cui, Y., & Pan, S. (2026). Model Predictive Control for Coupled Indoor Air Quality and Energy Performance Based on Incremental Thermal Preference Learning: Experimental Validation in Office Environments. Sustainability, 18(1), 240. https://doi.org/10.3390/su18010240

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