Multi-Objective Optimization of Building Ventilation Systems Using Model Predictive Control: Integrating Air Quality, Energy Cost, and Environmental Impact
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 m<sup>2</sup> university building. The d...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/1/451 |
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| Summary: | 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 m<sup>2</sup> university building. The developed model includes multiple objective model predictive control (MPC) with three objectives: electricity cost, indoor air quality and CO<sub>2</sub> 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 CO<sub>2</sub> 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. |
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| ISSN: | 2076-3417 |