Model-Aware Predictive Control for Occupant-Centric Environment Optimization in Room-Level Scenarios

The global drive towards sustainability and energy conservation has accelerated the development of intelligent buildings utilizing building management system (BMS). Occupants have profound impacts on building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of BMS, which thus give rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environment control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control framework named MAPC, which can construct credible models with limited data and provide room-level control strategies allowing for occupant comfort and energy efficiency. Its technological innovations are twofold. On the one hand, we design a model construction and fine-tuning method combining data-driven subspace projection approach with physical priors, which can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism, which enables room-level global optimal control considering dynamic occupant comfort requirements and energy usage. Experimental results obtained on a typical duplex apartment dataset demonstrate that MAPC is able to provide room-level control strategies based on dynamic occupant requirements and user preferences, achieving improved occupant comfort and energy efficiency. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets.