Multi-Objective Optimization of Building Ventilation Systems Using Model Predictive Control: Integrating Air Quality, Energy Cost, and Environmental Impact

Abstract

This paper presents a flexible heating, ventilation and air conditioning (HVAC) modeling framework developed for building digital twin implementation. The framework is showcased for the modeling and simulation of four ventilation systems in a 8500 m2 university building. The developed model includes multiple objective model predictive control (MPC) with three objectives: electricity cost, indoor air quality and CO2 emission attributed to electricity consumption. A control strategy comparison is conducted between several MPC solutions with different objective weightings and a rule-based control strategy, which emulates the current system control. A novel approach for air quality evaluation is proposed and used for the MPC modeling and strategy comparison in this study. In this comparison, a “balanced” MPC strategy reduces energy costs by 18% compared to rule-based control while also providing significantly better air quality. An economic strategy achieves 24% savings with some air quality reduction, while an air-quality-focused strategy provides nearly “perfect” air quality with 11% savings. Finally, an environmental strategy shows the potential for prioritizing CO2 emissions over electricity costs. In this way, the strategy comparison illustrates the potential of MPC for the efficient operation and flexible objective prioritization according to stakeholder interests.