The aim of this study was to propose a new multi-objective optimization (MOO) of a ventilation controller which finds the optimal set points for simultaneously improving the indoor air quality (IAQ) and increasing the energy efficiency in buildings under changes in outdoor air quality and climate change. The outdoor weather information, such as ambient temperature, humidity, solar radiation and wind speed, was applied and treated as external disturbances in the building system. Two control strategies including proportional-integral (PI) control and multivariate model predictive control (MPC) were implemented and compared while controlling the indoor air temperature and CO2 concentration in the targeted building system. A control performance assessment (CPA) technique was proposed and implemented for monitoring the MPC controller performance. With the goal of determining the optimal set points for the MPC controller, the multi-objective genetic algorithm was developed to enhance the energy consumption as well as to keep the IAQ within an acceptable range. The results indicate that the performance of the MPC controller with the optimized set points is superior to that of the PI controller in indoor building systems. More specifically, the MPC controller with the optimal set points for the indoor temperature and CO2 control could reduce energy consumption by 5.22% and CO2 content by 13.39% in comparison to the PI controller. In addition, the MPC controller equipped with MOO could be useful for building climate control depending on the variation of the outdoor air pollutants.
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