## 5 Conclusions  
This study focused on optimizing ACU control methodology using an intelligent fuzzy inference system (FIS), which was further enhanced using rough set theory (RST). The application of RST in this study was successful in refining the performance of the FIS by eliminating redundancy within the rule set, thereby reducing the total number of rules. The level of reduction of rules to those applicable to instructional rooms was about 99.72% (i.e., from 3357 rules to nine rules), without compromising accuracy, which remained at 98.94% when tested with actual historical data values and 88.57% when using all possible input condition values (i.e., indoor and outdoor environmental condition and occupancy rate data). This reduction enhanced the computational speed of the FIS, allowing the system to process inputs faster and make decisions in real-time within 1.06 s, as compared to 26.48 s (96.40% faster) when RST was not applied. For office spaces, the rule reduction rate was at 99.27% (i.e., from 550 to four), yielding an accuracy of 100.00% when using actual historical data values and 96.06% when tested using all possible values for input conditions. The system achieved an execution time of 0.85 s, which is 86.48% faster than when RST was not applied. Consequently, the optimized FIS yielded a control strategy that was simpler and more interpretable when compared to a conventional fuzzy system. Beyond the reduction in complexity, the integration of RST also increased the responsiveness of the FIS to environmental changes. Through the elimination of irrelevant rules and focusing on the most influential combinations of indoor and outdoor conditions, the RST-optimized FIS achieved better daily and steady-state power consumption periods, with respective averages of 25.56% (ranging from 1.59% to 60.06%) and 63.24% (ranging from 40.13% to 78.16%) per 1 \({}^{\circ}\)C increase in ACU setpoint from 22 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. This was computed using the cumulative sum technique, which allows for change point detection regarding power consumption and determines when the ACU has reached a steady-state(or minimal power consumption) condition. Sensitivity analysis further highlighted the effectiveness of the RST-optimized FIS in dynamically adjusting ACU setpoints. Comfort conditions were maintained even as the outdoor heat index fluctuated between 27 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, the indoor temperature between 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, and indoor humidity from 40% to 100%. This adaptive capability was observed in the non-optimized FIS, but with a relatively higher error rate, indicating the importance of RST in terms of enabling more precise control decisions. By prioritizing influential factors and discarding less relevant ones, the RST-optimized FIS exhibited better logical control over ACU operations. In the optimization of rules using RST, the integration of a heuristic search for rapid approximation, satisfactory and relative thresholds for adaptable rule induction, and rigorous validation methods such as K-fold cross-validation and stratified sampling guarantee an effective rule-generation process. This method balances the need for real-time performance and computational efficiency with the capability to produce significant and distinctive decision rules, making it suitable for diverse datasets, especially continuous data. Using the ROSE2 Rough Set Theory Simulator, all possible fuzzy rules were reduced, allowing for the generation of decision rules based on similarity through rule induction. This led to improved computational times by dealing with the numerous possible combinations of multiple membership functions defined for each input condition. Through RST, better interpretability was also achieved, as it focused on meaningful rules to cover the conditions present in the dataset while avoiding data overfitting.