## 4 Discussion
### Psychometric Chart: Visualization of Thermal Comfort Across Floors
The integration of RST effectively eliminates redundant rules, addressing the challenges relating to computational complexity observed in studies such as [21], where high network complexity and slower processing times limited the responsiveness of such systems. In comparison to studies such as [22; 29], which emphasized the maintenance of predicted mean vote (PMV) indices or indoor comfort ranges, this study utilized normalized energy savings (NES) as a more holistic and versatile metric. NES accounts for both daily and steady-state power consumption, showcasing significant energy savings, and was also validated with respect to the thermal comfort survey completed by occupants. For instance, while [PERSON] et al. (2019) have reported energy savings of 26% in smart buildings, this study achieved daily NES improvements ranging from 7.36% to 27.59%, depending on the room type, and steady-state NES values of up to 68.52% per degree increase in the ACU setpoint. Unlike studies such as [19], which were limited to specific HVAC setups, this research applies NES across diverse room types while accounting for environmental variables and occupancy rates. Furthermore, the ability to identify and analyze anomalies--such as increased energy use in computer rooms--provides actionable insights regarding ACU performance and operational inefficiencies. The summary of other HVAC optimization techniques presented in Table 20 highlights the energy savings and occupant comfort achieved through machine learning, control algorithms, neuro-fuzzy systems, and genetic algorithms. Machine learning methods, including random forest and occupancy detection algorithms, achieved energy savings of 15-23% by enabling real-time adjustments based on environmental and occupancy data, enhancing the responsiveness and efficiency of the system. Fuzzy-PID hybrid systems improved HVAC stability and responsiveness, reducing energy waste while delivering savings of 18-25%. These systems minimized oscillations and optimized temperature changes, thus ensuring greater comfort and efficiency. Adaptive neuro-fuzzy inference systems (ANFISs) achieved the highest energy savings (of up to 30%) by combining predictive modeling with real-time adaptive control, effectively adjusting the temperature and humidity to meet the needs of occupants. Similarly, genetic algorithms provided versatile solutions for tuning fuzzy rule parameters and balancing energy efficiency with occupant comfort, achieving energy savings of up to 30% in multi-objective optimization scenarios. Together, these techniques illustrate the benefits of advanced control strategies in terms of optimizing HVAC operations, reducing energy costs, and enhancing thermal comfort, with the choice of method depending on specific system requirements and operational goals. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimization Technique** & **Purpose** & **Techniques Used** & **Energy Savings Achieved [\%]** \\ \hline \multirow{8}{*}{Machine Learning} & \multirow{8}{*}{Predict occupant comfort; adjust HVAC settings dynamically based on indoor and outdoor conditions.} & 1. & Random Forest: Used to adjust ventilation control based on indoor air quality (e.g., PM2.5 and CO\({}_{2}\) concentrations). \\  & & & \\  &