## 3 Simulation Results
Again, higher temperatures lead to increased power consumption; however, the significant variability at higher temperatures and the presence of outliers at lower temperatures suggest additional hidden factors influencing the unit's operation. This complexity makes predicting power consumption more challenging. According to our deep review of the power consumption of the two primary cooling systems, it can be concluded that due to higher values for the residual components in certain hours, finding the accurate pattern for these systems is demanding and requires more information. However, leveraging the strengths of XGBoost can help to find the nonlinear relation between the input features and the power consumption of the cooling systems, covering the aforementioned challenge. First, Grid Search Optimization is used to find the optimal hyperparameters of this technique, according to which the best fit can be estimated. Figure 9 shows the actual and optimized XGBoost predicted power consumption of the ammonia cooling system for the test data. The subfigures demonstrate that the optimized XGBoost model accurately predicts the cooling system's power consumption by leveraging the three selected feature types, effectively capturing its consumption patterns. In this regard, Table 2 compares the model's performance against existing methods using various evaluation metrics. Figure 8: Power consumption range of CO\({}_{2}\) cooling system per various temperature bins. Figure 7: Power consumption range of ammonia cooling system per various temperature bins. As shown in Table 2, the proposed XGBoost model outperforms existing techniques across three error evaluation metrics. Additionally, its high R\({}^{2}\) value indicates strong accuracy despite variability in observed consumption data. A similar analysis was conducted for the other cooling system, and Figure 10 compares the predicted power consumption to the actual test data. Despite the high variability in power consumption and the presence of outliers, the proposed model successfully captures the unit's consumption pattern. Table 3 presents the model's performance evaluation using various error criteria. Despite high residual variability, Table 3 illustrates that the proposed model surpasses existing solutions in predicting the cooling system's power consumption. Furthermore, the high R\({}^{2}\) value confirms the model's accuracy across a wide range of data variations. To assess the explainability of the developed XGBoost model, SHAP values for the ammonia cooling system were calculated using the test data. These values visually summarize the feature importance across predictions, and are presented in Figure 4.0 \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline Proposed & 16.82 & 825.91 & 28.73 & 0.97 \\ Random Forest & 17.67 & 1000.76 & 31.63 & 0.96 \\ Linear Regression & 18.19 & 1033.77 & 32.15 & 0.96 \\ SVR & 26.19 & 1306.31 & 36.14 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of the effectiveness of the various methods in modeling the ammonia plant. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline Proposed & 22.29 & 1943.99 & 44.09 & 0.91 \\ Random Forest & 22.50 & 2181.32 & 46.70 & 0.89 \\ Linear Regression & 23.52 & 1991.05 & 44.62 & 0.90 \\ SVR & 30.00 & 2687.14 & 51.83 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of the effectiveness of the various methods in modeling the CO\({}_{2}\) plant. Figure 9: Comparison between the actual and predicted value across test data for the ammonia cooling system. As shown in Figure 11, the previous hour's power consumption emerges as the most influential feature. If the cooling system consumed a large amount of power in the prior hour, the model assumes inertia and predicts similarly high consumption for the current hour.