Model predictive control for managing indoor air quality levels in buildings

In recent years, stricter regulations aimed at improving indoor air quality have led to increased energy consumption in ventilation systems. This is particularly noticeable in the service sector, where high occupancy requires frequent air renewal. Therefore, it is crucial to apply control methods in these buildings to ensure proper indoor air quality and comfortable temperatures at minimal energy consumption and cost. However, addressing these operational requirements demands the utilization of advanced control methods and the integration of renewable energy sources. In this context, this article aims to design a model predictive control capable of managing indoor air quality levels to ensure occupants' comfort while minimizing operational costs. The novelty of this work lies in the development, for the first time, of a model predictive control that incorporates solar electricity production, changes in grid electricity prices, occupancy levels, and weather profiles. Additionally, several indoor CO2 concentration limits based on current regulations and building users' activities were employed to evaluate the model performance. Results demonstrated that model predictive control could achieve economic savings of up to 15 % when indoor CO2 concentration must be below 750 ppm compared to binary control modulation with photovoltaic panels. Moreover, the model effectively shifted ventilation necessities to off-peak electricity hours and fully harnessed energy generated by photovoltaic panels. Consequently, the presented model predictive control exhibited promising performance for potential applications in service sector buildings.