## 3 Results
### RST Rule Integration for ACU Setpoint Control Using RST and FL
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Room Type**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Rules**} & **Objects** & **Accuracy** & **Strength** & **Similarity** \\  & & & **Covered** & **[\%]** & **[\%]** & **[\%]** \\ \hline \multirow{8}{*}{Classroom} & 1 & (A\({}_{2}\) = emp) \(\rightarrow\) (D1 = OFF) & 21,274 & 100 & 100 & 100 \\  & 2 & (A\({}_{1}\) = min) \& (A\({}_{2}\) = max) \(\rightarrow\) (D1 = 22) & 270 & 100 & 35.86 & 100 \\  & 3 & (A\({}_{2}\) = extr) \(\rightarrow\) (D1 = 22) & 445 & 100 & 59.10 & 100 \\  & 4 & (A\({}_{2}\) = max) \(\rightarrow\) (D1 = 22) & 308 & 100 & 40.90 & 100 \\  & 5 & (A\({}_{1}\) = min) \& (A\({}_{2}\) = over) \& (A\({}_{6}\) = haza) & 923 & 100 & 28.42 & 100 \\  & 6 & (A\({}_{1}\) = min) \& (A\({}_{2}\) = high) \& (A\({}_{3}\) = slytw) & 125 & 100 & 3.85 & 100 \\  & & \& (A\({}_{6}\) = poor) \(\rightarrow\) (D\({}_{1}\) = 23) & & & & \\  & 7 & (A\({}_{1}\) = min) \& (A\({}_{2}\) = over) \& (A\({}_{6}\) = poor) & 295 & 100 & 9.08 & 100 \\  & 8 & A\({}_{1}\) = min) \& (A\({}_{2}\) = high) \& \(\rightarrow\) (D\({}_{1}\) = 23) & 705 & 100 & 21.71 & 100 \\  & 9 & (A\({}_{1}\) = min) \& (A\({}_{2}\) = ave) \(\rightarrow\) (D\({}_{1}\) = 23) & 617 & 100 & 19.08 & 100 \\  & 10 & (A\({}_{2}\) = over) \(\rightarrow\) (D\({}_{1}\) = 23) & 1698 & 100 & 52.28 & 100 \\  & 11 & (A\({}_{2}\) = high) \& (A\({}_{6}\) = haza) \& (A\({}_{8}\) = hzzd) & 444 & 100 & 13.67 & 100 \\  & & \(\rightarrow\) (D\({}_{1}\) = 23) & 738 & 100 & 22.72 & 100 \\  & 13 & (A\({}_{2}\) = high) \& \(\rightarrow\) (D\({}_{1}\) = 23) & 812 & 100 & 25.00 & 100 \\  & 14 & (A\({}_{1}\) = min) \& (A\({}_{2}\) = low) \& \(\rightarrow\) (D\({}_{1}\) = 24) & 419 & 100 & 58.44 & 100 \\  & 15 & (A\({}_{2}\) = mod) \& \(\rightarrow\) (D\({}_{1}\) = 24) & 271 & 100 & 37.80 & 100 \\  & 16 & (A\({}_{2}\) = low) \& \(\rightarrow\) (D\({}_{1}\) = 24) & 446 & 100 & 62.20 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classification rules and results using the LEM2 algorithm with a similarity relation. Of the 16 rules generated from the classroom data, several key candidates stood out for air conditioning unit (ACU) automation, based on their strength, similarity, and accuracy values. Rules 1, 3, 4, 10, 12, 13, 15, and 16 had high strength (>25%) and similarity (all 100%) values, making them reliable candidates for automating ACU behaviors, primarily based on the reduct \(A_{2}\), and ensuring both comfort and energy efficiency. Rule No. 14, despite its high strength value (58.44%), was excluded from the selected rules as its conditional attributes are based on ACU power consumption (\(A_{1}\)) and occupancy rate (\(A_{2}\))--factors that are insufficient to determine the ACU setpoint to ensure thermal comfort. Furthermore, Rules 2, 8, and 9 were excluded for similar reasons. The rest of the rules considered (i.e., Rules 5, 6, 7, and 11), though containing the reduct \(A_{2}\), were also excluded as they shared relationships with indoor air quality--a factor that does not directly affect thermal comfort based on [PERSON]'s formula for PMV and PPD. Of the eight rules generated from the computer room data, there were four key candidates for air conditioning unit (ACU) automation based on their strength, similarity, and accuracy values. These are the rules required for the automatic configuration of ACU settings based on the selected conditions. In particular, rules with high confidence and support values (100% for Rule 1, 65.22% for Rule 2, 62.75% for Rule 8, 37.25% for Rule 7, and 34.78% for Rule 3) were considered good predictors that could be cautiously used to control ACU operations based on the occupancy rate. The remaining rules (i.e., 4, 5, and 6), although containing \(A_{2}\), were dependent on either air quality conditions or current ACU power consumption and, despite their good strength (all above 28.06%), they were excluded from operational needs.