## 3 Results and Discussion
### Model Training Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE, kWh**} & \multicolumn{2}{c}{**MAPE, \%**} & \multicolumn{2}{c}{**RMSE, kWh**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{2-9}  & **Train.** & **Val.** & **Train.** & **Val.** & **Train.** & **Val.** & **Train.** & **Val.** \\ \hline AdaBoost & 31.35 & 53.75 & 8.46 & 19.94 & 39.46 & 86.75 & 1.00 & 0.57 \\ \hline XGBoost & 53.45 & 41.59 & 10.12 & 13.58 & 79.35 & 79.25 & 0.98 & 0.64 \\ \hline Random Forest & 43.79 & 40.59 & 7.93 & 12.90 & 66.58 & 81.71 & 0.99 & 0.61 \\ \hline LightGBM & 61.94 & 37.86 & 11.77 & 12.38 & 91.22 & 75.61 & 0.97 & 0.67 \\ \hline Ridge & 111.39 & 43.52 & 19.18 & 14.70 & 175.35 & 84.25 & 0.90 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of power consumption forecast for the compressor station in the first experiment. In the third experiment, the data on the gas transportation plan (daily values of power consumption by _GCUs_, _ACUs_, and _ACU_2_) were added to the features used in the first experiment. The results are shown in Table 5.  
The best results in terms of MAPE on the test set were demonstrated by LightGBM and XGBoost, with values of 3.20% and 3.44%, respectively. Comparing other metrics shows that XGBoost has the best result: these results have higher R\({}^{2}\) and lower values of MAE and RMSE. Hence, considering production process factors reduces MAPE by 13.37% compared to the expert forecast. The R\({}^{2}\) of 0.98 on the testing set indicates a strong relationship between input parameters and model results. In other words, the model explains 98% of the variance in the compressor station's power consumption, with only 2% of the variance remaining unexplained by the model. This means that the model can make very accurate forecasts based on previously unknown data. Comparing the MAE of 10.24 kWh and RMSE of 17.74 kWh with the MAE and RMSE values in the first experiment, which were 37.86 kWh and 75.61 kWh, respectively, shows that considering production process factors reduces the forecast error relative to all analyzed metrics. At the same time, models based on random forest and LightGBM are characterized by good learning results (the difference between the mean absolute percentage error on the test set is less than 4% compared to the mean absolute percentage error on the training set). The results of models based on AdaBoost and Ridge also show improvement. In the fourth experiment, all features were considered, except for the production process index. The results are presented in Table 6.  
The best MAPE on the testing set was achieved by XGBoost and LightGBM, with values of 3.70% and 3.25%, respectively. The higher MAPE compared to the third experiment can be attributed to inaccurate consideration of weather data. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE, kWh**} & \multicolumn{2}{c}{**MAPE, \%**} & \multicolumn{2}{c}{**RMSE, kWh**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{2-9}  & **Train.** & **Val.** & **Train.** & **Val.** & **Train.** & **Val.** & **Train.** & **Val.** \\ \hline AdaBoost & 29.26 & 50.51 & 7.94 & 17.91 & 36.47 & 85.28 & 1.00 & 0.58 \\ \hline XGBoost & 71.57 & 51.84 & 13.21 & 17.88 & 108.91 & 87.79 & 0.96 & 0.56 \\ \hline Random Forest & 40.73 & 42.46 & 7.43 & 13.35 & 62.08 & 83.85 & 0.99 & 0.59 \\ \hline LightGBM & 48.68 & 38.61 & 9.50 & 11.99 & 70.29 & 78.06 & 0.98 & 0.65 \\ \hline Ridge & 110.23 & 49.61 & 18.81 & 16.81 & 172.08 & 87.09 & 0.91 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of forecasting the power consumption of the compressor station in the second experiment.