Operative forecasting of fuel gas consumption in gas transportation companies

In the current context of energy resource conservation and increased effi ciency of gas transportation systems, developing approaches to improve the accuracy of fuel gas consumption forecasting at compressor stations is a pressing task. This paper analyzes approaches and algorithms for forecasting the volumes of gas needed for the technological and internal needs of compressor stations during gas compression within the gas transportation organizations of Gazprom PJSC. A classifi cation of gas consumption for technological needs and losses is presented, emphasizing the importance of managing fuel gas consumption to optimize the cost of natural gas transportation. The goal of this study is to develop an approach for operational forecasting of fuel gas consumption at compressor stations of gas transportation organizations, aimed at increasing economic effi ciency and reducing operating costs. To achieve this goal, the following tasks were undertaken: analysis of existing forecasting methods, investigation of data processing techniques for detecting errors and anomalies, and comparison of various regression models to ensure high forecast accuracy. The study employed data cleaning and preprocessing methods, including the Isolation Forest method for anomaly detection, as well as various regression models such as multiple linear regression, RandomForestRegressor, CatBoostRegressor, and XGBoost. Data segmentation was performed using cluster analysis (KMeans), which allowed for improved model accuracy. Forecast accuracy was assessed using t-tests, F-tests, and the mean absolute percentage error (MAPE) metric. The results of the study confi rmed the high accuracy of the proposed approach, demonstrating its potential for optimizing fuel costs in gas transportation organizations.