Optimizing the operation and control of air conditioning systems in public buildings is a critical technical measure for ensuring healthy and comfortable indoor environments while reducing building carbon emissions. The interdependencies among control objectives pose a key challenge affecting operational effectiveness. This paper proposed a Multi-objective optimization control method (MOOCM) for indoor environmental parameters based on a Non-dominated sorting genetic algorithm, which aims to achieve intelligent control of air conditioning systems by simultaneously satisfying thermal comfort, improving indoor air quality, and enhancing operational energy efficiency. To validate the effectiveness and applicability of the proposed MOOCM, comparative experimental studies were conducted between the proposed control method and traditional fixed-temperature setpoint control. The results demonstrated that the proposed control method achieves synergistic optimization of indoor thermal comfort, indoor air quality, and air conditioning system operational efficiency. The average COP of the HVAC energy efficiency enhancement control method higher than the traditional control method, effectively reducing energy consumption. Regarding indoor thermal comfort, the proportion of comfortable votes under the thermal comfort optimization control method and the energy efficiency enhancement control method is 58.6% and 57.1%, respectively, exceeding the 50.0% under the traditional control method. Regarding air quality, the average indoor CO 2 concentrations under the traditional control method, thermal comfort optimization control method, and energy efficiency enhancement control method are 766.56 ppm, 708.71 ppm, and 741.36 ppm, respectively. This demonstrates that the proposed control method can effectively reduce indoor CO 2 concentrations and improve indoor air quality.
Li et al. (Sun,) studied this question.
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