## 3 Results and Discussion  
### Model Training Results  
After the dataset was formed, Spearman's correlation coefficients were analyzed for the gas transportation plan features (daily power consumption values by GCUs and ACUs and their sums: _GCU_1_, _GCU_2_, _GCU_3_, _GCU_, _ACU_1_, _ACU_2_, _ACU_3, and _ACU_, respectively). Spearman's correlation coefficients were calculated according to the following:  
\[r=1-\frac{6\sum d_{i}^{2}}{n\cdot(n^{2}-1)}, \tag{24}\]  
where \(r\) is the [PERSON]'s correlation coefficient, \(d_{i}\) is the difference between two ranks of each feature, and \(n\) is the number of observations. [PERSON]'s correlation coefficients for data on planned gas transportation are shown in Figure 9.  
Figure 9: [PERSON]’s correlation coefficients. Removing values with [PERSON]'s correlation coefficients greater than 0.70 from the data set can increase generalization properties. Therefore, only total daily values of power consumption by _GCUs_ and _ACUs_, as well as daily values of second _ACU_2_, were considered in the experiments. Based on the initial data, the following features were chosen for model training:  
* Retrospective data on actual power consumption (power consumption (hour--h), where h--48, 60, 72, 84, and 96 are the hours before the forecast hour);
* Calendar features (hour, day, day of week, and month);
* Data on the gas transportation plan (the production process index and total daily power consumption by GCUs, ACUs, and ACU2);
* Weather factors (air temperature, atmospheric pressure, humidity, and wind speed). Experiments were conducted considering various features. In addition to comparing the training results of the models, the errors of the expert forecast were used as reference values: MAPE of 16.81% and MAE of 105 kWh. In the first experiment, only retrospective values of power consumption \((y_{i-48},\ y_{i-60},y_{i-72},y_{i-84},y_{i-96},X)\) and calendar features were considered. The experimental results are shown in Table 3.  
LightGBM demonstrated the best results on the test dataset by the MAPE criterion at 12.38%. It indicates an increase in forecast accuracy in relation to the expert forecast of 4.43%. It should be noted that the R\({}^{2}\) for the best result is only 0.67. Consequently, it can be concluded that the LightGBM-based model provides explanations for a significant part of the power consumption of the compressor station dispersion. However, even though parameters substantially contribute to the model, the model makes errors when predicting individual hours of power consumption. In other words, the stochastic nature of power consumption cannot be accurately described when only retrospective power consumption and calendar parameters are considered. It is also important to note that underfitting is observed in the Ridge model training. The underfitting of the model is explained by the fact that the model fails to find a function that adequately describes the data. Overfitting is observed in the training of models based on other methods. Model overfitting is explained by the fact that the model fails to generalize new data: it accounts for the specifics of the training data rather than the underlying patterns. Moreover, the MAPE of the AdaBoost model exceeds the MAPE of the expert forecast. In the second experiment, weather data were added to the features used in the first experiment. The training results are presented in Table 4.  
LightGBM demonstrated the best results using the MAPE criterion of 11.99%. The addition of weather data does not lead to a significant improvement in results in comparison with the results of the first experiment. This can be explained by the fact that hourly weather data were obtained through linear interpolation, assuming that the parameter changes were linear, whereas, in reality, the parameters may change nonlinearly between measurements. In order to prevent this unfavorable outcome, a large dataset of weather data should be used to analyze changes in weather parameters.