A Predictive Control Strategy to Maximize Energy Savings While Maintaining Indoor Air Quality in Commercial Buildings

Before the COVID-19 pandemic, buildings accounted for more than 40% of the total energy. The need for improving indoor air quality (IAQ) not only during the pandemic but also in the post-pandemic era presents a unique opportunity for innovation that meets the needs in both environmental health and building energy efficiency. Carbon-dioxide concentration has been widely used for modeling and quantifying IAQ. Model predictive control (MPC) has been demonstrated promising potential in improving the energy performance of building heat ventilating and air conditioning (HVAC) systems. However, little research fell on both IAQ and energy efficiency improvement. In the paper, we developed a data-driven, coupled—IAQ and energy simulation method that combines thermal comfort and indoor air quality, specifically CO2. A new MPC algorithm was developed correspondingly to improve both energy-saving and indoor health. The results of a one-week, national-wide cooling season simulation indicate at most 50% energy savings while maintaining less than 700 PPM indoor CO2.