## 3 Results  
This study examined two primary ACU operating modes--namely the cool mode and the dry mode--in order to assess their effects on energy consumption and occupant comfort across setpoints ranging from 22 \({}^{\circ}\)C to 26 \({}^{\circ}\)C. In the cool mode, the ACU prioritizes temperature regulation by maintaining a fixed cooling setpoint, with the compressor continuously adjusting its speed to meet cooling demands, as outlined in ASHRAE Standard 55 (2020). In the dry mode, it targets indoor humidity reduction by intermittently cycling the compressor on and off, leading to less precise temperature control. The power consumption and environmental/air quality condition data, before being used to identify factors affecting ACU performance, were first subjected to dataset preparation. This involved conversion into a numeric format to ensure their compatibility with analytical methods. Missing values were addressed by replacing them with a default  
Figure 2: Distribution of input variables measured in office spaces. value (e.g., 0) in order to prevent interruptions in subsequent calculations. To simplify the data, near-zero variance predictors with variances below a threshold of \(1\times 10^{-4}\) were identified as contributing minimally to the model and were considered for removal. This step enhances the computational efficiency and focuses the dataset on impactful predictors. To explore relationships between variables, a correlation matrix was calculated, highlighting highly correlated pairs (\(r>0.8\) or \(r<-0.8\)) that may introduce multicollinearity. Redundant variables within these pairs were also considered for removal in order to streamline the dataset. The correlation matrix was visualized as a heatmap, offering an intuitive understanding of predictor relationships. Example heatmaps for the classroom and office space datasets are illustrated in Figure 3.  
Table 8 summarizes variables or predictors in instructional rooms with high correlation coefficients (all at \(p\)-values of 0). The remaining predictors for power values were indoor CO\({}_{2}\), indoor wet bulb temperature, occupancy data, outdoor CO\({}_{2}\), outdoor heat index, and differential values of indoor and outdoor temperature and humidity. Table 9 summarizes the variables or predictors in office spaces with high correlation coefficients. The remaining predictors for power values were indoor CO\({}_{2}\), indoor wet bulb temperature, outdoor CO\({}_{2}\), outdoor heat index, and differential values of indoor and outdoor temperature and humidity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictor 1** & **Predictor 2** & **Correlation Coefficient** & **Basis of Selection** \\ \hline Indoor CO\({}_{2}\) Level & Indoor Total Volatile Organic & 0.999999794 & Indoor CO\({}_{2}\) represents indoor occupancy. \\ Indoor Dry Bulb Temperature & Indoor Heat Index & 0.885954147 & None: Has correlation with Wet Bulb Temperature \\ Indoor Wet Bulb Temperature & Indoor Wet Bulb Temperature & 0.936148863 & Combined temperature and humidity to represent felt temperature \\ Indoor Wet Bulb Temperature & Indoor Wet Bulb Temperature & Indoor Wet Bulb Temperature: Combined temperature and humidity to represent felt temperature \\ Indoor Heat Index & Indoor Wet Bulb Temperature & 0.931567138 & humidity to represent felt temperature \\ Outdoor CO\({}_{2}\) Level & Outdoor Total Volatile & 0.99999927 & CO\({}_{2}\) Level: Represents ventilation quality \\ Outdoor Dry Bulb Temperature & Outdoor Heat Index & 0.891582716 & Outdoor Bulb Temperature: Combined temperature and humidity to represent felt temperature \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Highly correlated predictors for instructional rooms. Figure 3: IAQ data correlation matrices for (**a**) Instructional rooms and (**b**) office spaces. Outlier detection was conducted in this study using the Z-score method, where every point with a Z-score greater than \(\pm 3\) was treated as an outlier.