## 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 & 14.75 & 23.40 & 4.22 & 8.98 & 18.35 & 36.84 & 1.00 & 0.92 \\ \hline XGBoost & 15.52 & 10.24 & 2.66 & 3.44 & 22.57 & 17.74 & 1.00 & 0.98 \\ \hline Random Forest & 14.31 & 17.93 & 2.42 & 5.60 & 22.02 & 36.75 & 1.00 & 0.92 \\ \hline LightGBM & 11.58 & 11.19 & 2.05 & 3.20 & 16.54 & 26.30 & 1.00 & 0.96 \\ \hline Ridge & 49.62 & 18.51 & 7.52 & 6.52 & 76.18 & 29.91 & 0.98 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of power consumption forecast for the compressor station in the third experiment. LightGBM yielded the best results. Consideration of the production process index without considering other production process factors reduced the MAPE by 5.81% compared to the expert forecast It is important to note that overfitting is observed in the training of models based on other methods. The summary results of the experiments are shown in Table 8. The absolute and relative improvement in the forecast accuracy was estimated in relation to the MAPE of the expert forecast (16.81%). Figures 10 and 11 depict the power consumption graphs for the third experiment, which yielded the lowest error values, clearly illustrating the high accuracy of the forecast. \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}{**R2**} \\ \cline{2-9}  & **Train.** & **Val.** & **Train.** & **Val.** & **Train.** & **Val.** & **Train.** & **Val.** \\ \hline AdaBoost & 14.73 & 23.35 & 4.21 & 9.10 & 18.31 & 36.04 & 1.00 & 0.93 \\ XGBoost & 15.57 & 10.43 & 2.71 & 3.70 & 22.38 & 17.12 & 1.00 & 0.98 \\ \hline Random Forest & 14.35 & 17.69 & 2.41 & 5.52 & 22.04 & 36.67 & 1.00 & 0.92 \\ \hline LightGBM & 11.53 & 11.31 & 2.50 & 3.25 & 16.39 & 25.88 & 1.00 & 0.96 \\ \hline Ridge & 49.55 & 19.33 & 7.59 & 6.72 & 75.75 & 31.47 & 0.98 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of power consumption forecast of the compressor station in the fourth experiment. \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}{**R2**} \\ \cline{2-9}  & **Train** & **Val** & **Train.** & **Val.** & **Train.** & **Val.** & **Train.** & **Val.** \\ \hline AdaBoost & 32.47 & 45.48 & 7.69 & 15.45 & 40.45 & 75.87 & 1.00 & 0.67 \\ \hline XGBoost & 54.49 & 42.11 & 10.43 & 14.62 & 80.59 & 69.50 & 0.98 & 0.72 \\ \hline Random Forest & 40.52 & 40.41 & 7.61 & 14.11 & 60.65 & 74.70 & 0.99 & 0.68 \\ \hline LightGBM & 60.42 & 35.02 & 10.52 & 11.00 & 89.70 & 67.04 & 0.97 & 0.74 \\ \hline Ridge & 104.89 & 63.08 & 18.93 & 26.04 & 155.47 & 85.58 & 0.92 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of power consumption forecast of the compressor station in the fifth experiment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Model** & **MAPE, \%** & **Improvement of Forecast Results, \%** & **Improvement of Forecast Results, p.u.** \\ \hline
1 & LightGBM & 12.38 & 4.43 & 0.26 \\ \hline
2 & LightGBM & 11.99 & 4.82 & 0.29 \\ \hline
3 & XGBoost & 3.44 & 13.37 & 0.80 \\ \hline
4 & XGBoost & 3.70 & 13.12 & 0.78 \\ \hline
5 & LightGBM & 11.00 & 5.81 & 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary results of experiments on short-term forecasting of power consumption by a compressor station. It should be noted that LightGBM demonstrated better results than other machine learning models for a small number of training features.