## 4 Conclusions  
The algorithm for improving the safe operation of gas industry enterprises and the energy efficiency of their production processes using a multi-agent approach was proposed in this article. This approach considers the interdependence of various production processes. The developed method was applied to solve the short-term power consumption forecasting problem for a compressor station. The research indicates that the accuracy of short-term power consumption forecasts can be enhanced through the modeling of production processes using a multi-agent approach. The experiments were conducted using various ensemble models with different features on real three-year data of a compressor station. It was found that consideration of production process factors can increase forecasting accuracy by 13.37%. Specifically, MAE, obtained by XGBoost, was decreased from 105 kWh of expert forecast to 15.57 kWh. In comparison with the experiment, where only retrospective power consumption and production calendar were considered, R\({}^{2}\) increased from 0.67 to 0.98, MAE decreased from 61.94 kWh to 15.57 kWh, and RMSE decreased from 91.22 kWh to 22.39 kWh. It was also discovered that LightGBM provides better results for models built on a dataset with a small number of input features. The SHAP algorithm was applied to enhance the interpretability of the results. The use of this algorithm can increase experts' trust in the results provided by intelligent information systems. It should be noted that although SHAP facilitates the interpretation of machine learning results by visualizing parameters, this algorithm does not allow for complete explainability of the results. Therefore, the proposed method can only be used if an expert with competencies in enterprise consumption forecasting interacts with the explanation results. Further research is planned to focus on the short-term forecasting of self-generation in oil and gas industry enterprises, scheduling of controllable load, scheduling of energy-storage systems, and experiments on data from other oil and gas industry enterprises. In \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**LightGBM**} & \multicolumn{2}{c}{**XGBoost**} \\ \hline
**Feature** & **Feature Importance** & **Feature** & **Feature Importance** \\ \hline hour & 0.1995 & air-cooling unit & 0.5068 \\ \hline air-cooling unit & 0.1790 & power consumption (hour—48) & 0.3419 \\ \hline power consumption (hour—48) & 0.1600 & compressor unit & 0.0437 \\ \hline compressor unit & 0.1100 & air-cooling unit 2 & 0.0316 \\ \hline power consumption (hour—60) & 0.0985 & power consumption (hour—60) & 0.0265 \\ \hline power consumption (hour—96) & 0.0610 & power consumption (hour—96) & 0.0155 \\ \hline air-cooling unit 2 & 0.0510 & hour & 0.0117 \\ \hline power consumption (hour—72) & 0.0470 & month & 0.0087 \\ \hline power consumption (hour—84) & 0.0440 & day & 0.0070 \\ \hline day & 0.0325 & power consumption (hour—84) & 0.0037 \\ \hline month & 0.0090 & power consumption (hour—72) & 0.0025 \\ \hline weekday & 0.0085 & weekday & 0.0003 \\ \hline \end{tabular}
\end{table} 
Table 9: Feature importance. addition, the developed method is planned to be applied to consider production processes for other optimization problems in power systems. Conceptualization, A.I.S., A.I.K. and P.V.M. ; methodology, A.I.S. and P.V.M. ; software, A.I.S. ; validation, A.I.S. and P.V.M. ; formal analysis, A.I.S. and A.I.K. ; investigation, A.I.S., A.I.K. and P.V.M. ; resources, S.A.E. ; data curation, A.I.S. and S.A.E. ; writing--original draft preparation, A.I.S. ; writing--review and editing, A.I.S. and P.V.M. ; visualization, A.I.S. ; supervision, A.I.K. and P.V.M. ; project administration, A.I.K. and S.A.E. ; funding acquisition, A.I.K. All authors have read and agreed to the published version of the manuscript. This research was carried out within the state assignment with the financial support of the Ministry of Science and Higher Education of the Russian Federation (subject No.