## 3 Results
To model the building-specific system more accurately, a better understanding of the systems reaction times/delays and the use of a 20% opening strategy is required. The strategy comparison unsurprisingly shows that MPC is significantly more energy efficient than rule-based control for ventilation system operation. While providing significantly better air quality, the \"Balanced\" MPC controller managed to reduce energy consumption by 18%. This result is comparable to similar studies of MPC implementation in HVAC building systems, which typically show savings in the range of 10-30% compared to baseline control [10]. With a more cost-focused approach, the \"Economic\" MPC strategy managed to reduce energy consumption by 24% compared to the baseline, but provided notably worse air quality. On the other hand, the \"Air quality\" MPC strategy provided almost perfect air quality according to the KPI evaluation while still reducing electricity usage by 11%. The \"Environmental\" MPC strategy showed some potential for prioritizing CO\({}_{2}\) emission with a 4% emission reduction compared to the \"Economic\" strategy, but the CO\({}_{2}\) factor (emission from electricity use) is closely correlated to the electricity price as seen in Figure 8. In this way, the four explored combinations for relative MPC weightings produce significantly differing system control strategies, which each optimize according to their different prioritization in system performance metrics. This demonstrates the ease and effectiveness of objective weighting customization with a MPC strategy, which allows for flexible prioritization of performance metrics according to stakeholder interests. The developed MPC is quite computationally demanding despite significant efforts to reduce optimization complexity and improve solver performance. This seems to be a common concern for MPC application in HVAC, due to the non-linearity and complexity of HVAC systems. One interesting observation is that the developed MPC handles the KPI threshold poorly with imperfect information. The applied solution is to lower the optimizations KPI threshold a bit in scenarios where air quality is prioritized, but a better (and more computationally demanding) approach would be robust optimization, where several potential future occupancy schedules are explored at each time step and the optimal solution accounts for all potential futures and their likelihoods. The KPI representing air-quality-related discomfort was established for this study, since no standard methods for measuring this metric were found in the literature. It functioned well for scenario comparison, but puts a large emphasis on the arbitrarily chosen CO\({}_{2}\) threshold, especially when air quality is valued highly. The challenge is such that the effect of CO\({}_{2}\) on human comfort and cognitive ability is not well documented for low CO\({}_{2}\) concentrations. Based on the study by [PERSON] et al. [22], it seems more reasonable to view any reduction of CO\({}_{2}\) contraction as advantageous and thus use a threshold of \(C_{T}\) = 0 ppm. This approach would also be more appropriate for MPC optimization. Moreover, the approach of relying solely on CO\({}_{2}\) concentration for air quality evaluation is suboptimal and more elaborate considerations of airborne particulate matter, organic gases and inorganic gases should be incorporated in models for systems where these are measured. In such cases, the air quality evaluation method could be based on the thresholds proposed by the \"WELL Building Standard\" [29], which is an international \"healthy building\" certification program with widespread usage. The developed model provides realistic and practical results, effectively capturing the key dynamics of the ventilation system. While some estimates and assumptions are incorporated, these align with common practices in system modeling to balance complexity and computational efficiency.