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.

As the popularity of fresh air systems (FAS) in residential buildings increases, exploring the behavioral characteristics of their use can help to provide a comprehensive understanding of the potential for demand flexibility in residential buildings. However, few studies in the past have focused on the personalized usage behavior of FAS. To fill this gap, this study proposes a method based on data mining techniques to reveal the behavioral patterns of FAS usage and the motivations behind them, including motivational patterns, operation duration patterns, and human–machine interaction patterns, for 13 households in Beijing. The simultaneously obtained behavioral patterns, in turn, form the basis of association rules, which can classify FAS usage behavior into two typical residential user profiles containing user behavioral characteristics. This study can not only provide more accurate assumptions and inputs for behavioral stochastic models but also provide data support for the development and optimization of demand response strategies.