## 3 Results
### RST Rule Integration for ACU Setpoint Control Using RST and FL
The rules derived from the laboratory room data provided an interesting rule design for the automatic control of an air conditioning unit (ACU), specifically based on \(A_{2}\). All three rules revealed 100% accuracy and similarity, meaning that they perfectly classified the objects they refer to. In particular, Rule 1 stands out as it ensures that the ACU shuts down efficiently when the condition is met. Table 11 delineates the rules under consideration for the automation of an air conditioning unit (ACU) in office spaces, with an emphasis on their predictive strength, similarity measures, and accuracy. In particular, four out of six rules were particularly viable candidates for ACU automation in shared office spaces due to their high precision and robust prediction rates based on factors affecting thermal comfort, particularly temperature. Taken together, these rules--namely Rules 1, 4, 5, and 6--were considered strong candidates for ACU automation. On the other hand, the remaining rules (2 and 3), although containing the outdoor heat index as a core attribute, were also dependent on additional conditions such as indoor dry bulb temperature (\(A_{5}\)) and carbon dioxide level (\(A_{6}\)), as in the case of Rule 2, and TVOC level, as in the case of Rule 3. Rules 2 and 4 suggest an ACU setting of 23 \({}^{\circ}\)C when the outdoor heat index is between 21 \({}^{\circ}\)C and 27 \({}^{\circ}\)C (warm) or 39 \({}^{\circ}\)C and 51 \({}^{\circ}\)C (danger), respectively. In the case of office spaces with solo occupancy, three key rules out of five candidates were identified based on their strength, similarity, accuracy, and relevance to thermal comfort. The most significant candidates for ACU automation were Rules 1 and 5, but Rule 3 was also considered given its resemblance with Rule 5 for a shared office space. Rule 2 had 69.11% strength but was excluded as it includes a conditional attribute related to a carbon dioxide level in the range of 350 to 450 ppm (ideal range). This rule did not appear for instructional rooms, except for classrooms, although at a very low strength (9.08-13.67%). Rule 4 contains a decision rule based on both the indoor relative humidity level (66 to 70%) and normal carbon dioxide level (451 to 1000), but had a rule strength of only 0.01%. The key classification rules using the ROSE2 LEM2 algorithm with a similarity relation were adopted for the development of a fuzzy logic controller for ACUs in instructional rooms (regardless of room type) and office spaces (both shared and solo). Rule 9 for instructional rooms was used additionally to define rules for empty spaces, as all other rules were related to the occupancy rate. No additional rule was imposed for office spaces, as the room occupancy rate had already been excluded from the conditional attributes. Table 12 provides a comparative evaluation of the performance of fuzzy inference systems (FISs) based on different datasets, rule sets, and FIS types implemented in instructional rooms. When using the complete range of possible input combinations with 3357 rules, the general FIS achieved a low error rate of 29.48% for 1187 test results, with a total execution time of 26.27 s. On the other hand, the RST-optimized FIS with only nine rules (i.e., a 99.72% reduction in the number of rules), using the same dataset, showed a lower error rate of 11.43% and a significant reduction in execution time, taking only 1.82 s. Using actual historical data, the generalized FIS had an average error rate of 1.42% with a processing time of 14.63 s, while the RST-optimized FIS had an improved average error rate and processing time of 1.06% and 0.97 s, respectively. While the RST-optimized FIS exhibited a higher error rate on the comprehensive dataset, it showed superior performance, with perfect accuracy, and a significantly reduced execution time when applied to historical data. This highlights the efficiency and precision of the optimized FIS in real-world scenarios.