## 1 Introduction  
Oil and gas enterprises use special energy programs to meet the requirements of critical infrastructure operations. These programs are aimed at reducing energy resource costs and consumption, as well as improving the energy efficiency and social responsibility of personnel/workers, which in turn affects energy efficiency [1]. One of the promising measures that can be implemented to enhance the energy efficiency of production processes in the gas industry is the development and implementation of a short-term (1-3 days) load-forecasting system [2, 3]. Forecasting of power consumption is essential for planning the operation of power systems [4]. Many countries are introducing economic incentives to promote demand-responsive power consumption [5]. One of the methods encouraging enterprises to plan their daily power consumption schedules is the wholesale electricity and capacity market [2]. By connecting to such a market, gas industry enterprises can benefit from lower tariffs than those on the retail market, provided that accurate power consumption forecasting is taken into account, as electricity tariffs include charges for deviations of actual consumption from planned schedules. The consumption patterns of gas industry enterprises often have a high aperiodic component of the load, which requires consideration of numerous production factors, making the process of power consumption forecasting labor-intensive [3]. Thus, the introduction of short-term power consumption forecasting system can enable:  
* Flattening of the power consumption curve;
* Optimizing the operation of self-generation units and energy-storage systems;
* Optimizing maintenance, repairs, and other support systems;
* Minimization of power consumption costs. Optimizing the operation of self-generation units and energy-storage systems can be achieved because their purpose is to manage the power consumption schedule, cover the enterprise's own needs, and ensure power supply quality [6]. Optimizing the support system processes becomes possible because increased accuracy in short-term power consumption forecasting allows the enterprise to develop an optimal maintenance schedule based on peak load hours [7]. Cost minimization can be achieved by regulating tariffs on the wholesale electricity and power market through load planning and controllable load consumer scheduling due to the fact that the wholesale electricity and power tariffs incentivize off-peak power consumption. Additionally, since part of the electricity tariff accounts for deviations in consumed power from the forecasted amount, improving forecast accuracy can help reduce electricity costs [2; 8]. Implementing this measure is complicated by the need to account for the influence of numerous production process factors on the power consumption schedule of gas industry enterprises [8]. Generally, existing studies are aimed at short-term forecasts of power consumption in relation to power systems, microgrids, and residential power demand [9; 10]. Due to the high proportion of periodic components in electricity consumption processes within power systems, the average forecast accuracy reaches 98-99% [3; 10; 11; 12; 13]. For short-term (1-3 days) forecasting of power consumption schedules, various methods are applied, which can be divided into deterministic (statistical) methods and machine learning methods. The former include seasonal models [14] and autoregression-based methods [15; 16; 17]. Machine learning methods can account for many factors, including weather [9; 18], production [19], and their interdependencies [20]. Studies on short-term power consumption forecasting for industrial enterprises to adjust power consumption in real-time based on demand within a microgrid are presented in [21; 22]. The advantages of accounting for different factors, not just the seasonal factor, while using machine learning methods compared to deterministic ones have been demonstrated in studies [20; 23; 24].