## 2 Materials and Methods
### Initial Dataset  
The initial dataset for this article included data from the automated commercial electricity metering system (ACEMS) of the gas industry enterprises and weather data. Data from the ACEMS included three-year values (791 days):  
* Hourly power consumption by the entire compressor station;
* Daily power consumption by ACUs of each compartment;
* Daily power consumption by GCUs of each compartment. Hourly power consumption data for each month was provided in separate files. Daily consumption data of ACUs and GCUs for each day was presented in separate files. The data had missing values. The statistical characteristics of power consumption for the compressor station under consideration are shown in Table 1.  
Figure 2: The main elements of typical compressor station. Analysis of the distribution of hourly power consumption values for the entire period shows a large standard deviation of 541.3 kWh compared to the average value of 675.8 kWh. The difference between the maximum consumption in the winter (2837 kWh) and the minimum in the summer (99 kWh) is 2738 kWh. The analysis of the statistical characteristics showed that the following factors are to be considered for the short-term power consumption forecast:  
* The dependence of power consumption on the operating modes of GCUs and ACUs;
* The non-stationarity nature of gas transportation processes;
* The seasonality factor. Figure 3 presents a fragment of the hourly power consumption for the compressor station from December 2020 to January 2021, demonstrating the high stochasticity of power consumption. Practice in working with data shows that sometimes, due to low automation of processes, it is not possible to obtain values for production process factors. Therefore, the production process index was introduced by the authors to account for the magnitude of power consumption. By the production process index, we mean a value that indirectly accounts for production processes. Its value was 1 for power consumption greater than 1 MWh; otherwise, it was 0. The value of 1 MWh was selected based on an analysis of the consumption load curve. While we do not provide a specific methodology for selecting this value, we demonstrate that its use improves the accuracy of power consumption forecasting. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Season/Year**} & \multicolumn{4}{c}{**Statistical Characteristics**} \\ \cline{2-5}  & **Average Value,** & **Standard** & **Min Value, kWh** & **Max Value, kWh** \\  & **kWh** & **Deviation, kWh** & **Min Value, kWh** \\ \hline \hline \multicolumn{5}{c}{Seasonal consumption} \\ \hline Whole dataset & 675.8 & 541.3 & 99.4 & 2837.0 \\ \hline Summer & 270.4 & 155.6 & 99.4 & 1556.0 \\ \hline Autumn & 983.9 & 604.9 & 125.3 & 2797.2 \\ \hline Winter & 1307.5 & 453.6 & 458.5 & 2837.0 \\ \hline Spring & 476.3 & 230.4 & 123.2 & 2017.8 \\ \hline \multicolumn{5}{c}{Consumption by year for one season (autumn)} \\ \hline
2019 & 430.2 & 108.1 & 266.0 & 737.0 \\ \hline
2020 & 1400.2 & 493.6 & 207.2 & 2797.2 \\ \hline
2021 & 511.7 & 288.7 & 125.3 & 1808.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical characteristics of power consumption of compressor station. Figure 3: Actual power consumption of compressor station from December 2020 to January 2021. Since we are forecasting the power consumption of a compressor station whose production processes are season-dependent, it is necessary to consider weather data. The data were taken for a weather station located 5 km from the compressor station from the archive of the rp5.ru website. This assumption of the use of weather data from the archive instead of the forecasted ones can be justified by the fact that meteorological parameters are additional factors, and the accuracy of weather forecasting is comparable to actual weather parameters. The meteorological data include:  
* Wind speed (m/s);
* Temperature (\({}^{\circ}\)C);
* Atmospheric pressure (mm Hg);
* Humidity (%).