## 4 Discussion
### Normalized Energy Savings (NES)  
While the peak method depends primarily on the direct peak detection algorithm and is computationally simpler, it is also sensitive to short-term fluctuations and sharp transitions, making it ideal for systems requiring rapid diagnostics and real-time control. However, it may miss the gradual changes in operational behavior. In contrast, the CUSUM method is more robust for long-term monitoring and trend analysis, effectively detecting subtle changes that may signal inefficiencies or faults. However, this method may lag in catching any sudden change as it relies on accumulation patterns. Therefore, the peak method is especially good for situations where the operational transitions are distinct and control is needed for short periods. On the other hand, the CUSUM method works better in cases requiring long-term diagnostics, preventive maintenance, and addressing gradually degrading system performance. Both methods complement each other in providing diagnostics and trend analysis for the performance optimization of systems. The operational efficiency, as an effect of changing the ACU setpoint, was quantified using measured and calculated ACU power consumption expressed as normalized energy savings (_NES_), allowing for the quantification of energy efficiency by comparing energy consumption at different ACU setpoints relative to a baseline condition. The baseline is typically defined as the energy consumption at the lowest setpoint temperature, such as 22 \({}^{\circ}\)C. NES helped in evaluating the percentage reduction in daily power consumption (\(P_{T}\)) when setpoints were increased, enabling a clear assessment of efficiency gains associated with the adjustment of ACU operations. This approach ensures consistency and comparability across room types and operational conditions. In this study, NES was applied to a variety of room types--namely classrooms, offices, laboratories, and computer rooms--under different ACU setpoints. Environmental factors such as humidity, temperature, and other conditions were integrated into the power consumption calculations. The formula for _NES_ is given in Equation (8). For each room type, energy consumption at higher setpoints (e.g., 23 \({}^{\circ}\)C, 24 \({}^{\circ}\)C, or 25 \({}^{\circ}\)C) was compared to the baseline at 22 \({}^{\circ}\)C. For instance, in classrooms, raising the setpoint from 22 \({}^{\circ}\)C to 24 \({}^{\circ}\)C resulted in a daily energy consumption reduction of 25.62%, demonstrating enhanced energy efficiency without compromising thermal comfort. The _NES_ analysis based on peak power consumption method analysis is summarized in Table 15, revealing distinct trends across room types. \[NES\ (\%)=\ \frac{P_{T}^{baseline}-P_{T}^{setpoint}}{P_{T}^{baseline}}\times 100 \tag{8}\]Incorporating NES based on both the average steady-state power (\(P_{SS}\)) and steady-state power period (\(T_{SS}\)) allows for a more holistic evaluation of ACU energy efficiency. This dual-metric approach not only enables the assessment of energy consumption during steady-state operations but also evaluates the duration for which the desired conditions are maintained, offering a comprehensive view of operational performance, as summarized in Table 16. A significant improvement of more than 50% in \(P_{SS}\) was observed in classroom and computer room contexts at 24 \({}^{\circ}\)C, while only 25% was achieved in the laboratory room context at 25 \({}^{\circ}\)C. An increase in \(T_{SS}\) for both classroom and computer room contexts was observed, suggesting that energy efficiency gains were achieved in the steady-state condition. Despite the energy savings achieved in the laboratory, there was no substantial improvement in its steady-state duration, implying that savings were achieved due to the low consumption of ACUs at higher setpoints and not because of an increase in steady-state duration.