Optimizing Comfort and Energy Efficiency: The Impact of Model Accuracy on Multi-Objective MPC

Buildings are responsible for ~30% of primary energy consumption, mainly because of Heating, Ventilation, and Air Conditioning (HVAC) systems. The usual ON/OFF controller tends to react to occupancy presence, causing discomfort and energy waste. Furthermore, these controllers usually focus on thermal comfort and disregard other comforts, such as air quality, visual, etc. due to their inability to handle complex multi-objective problems. In this context, Model Predictive Controller (MPC) presents a promising alternative for dynamic control of HVAC systems. However, the accuracy of the building’s thermal and air quality models greatly influences the MPC performance. This paper proposes simple models to develop a multi-objective MPC to minimize energy consumption while maintaining occupancy thermal and air quality comfort. Each models are developed using the limited data available. Since these models largely depend on occupancy information, a further study is conducted to analyze the impact of occupancy estimation accuracy on MPC performance. It is found that occupancy estimation models reach 98% of the optimum performance with a 90% accuracy.