## 3 Results and Discussion
### The Interpretation Examples  
For the third experiment, which yielded the best results, the SHAP algorithm was applied to models based on XGBoost and LightGBM. The results of applying the SHAP algorithm show the significance (weights) of the parameters that influenced the forecast of power consumption for each hour. These weights can be interpreted by an expert and used to make expert adjustments. The features are arranged from the bottom of the graph in ascending order of influence on the predicted value of power consumption \(f(x)\) relative to the average value across of power consumption of the whole dataset \(E[f(X)]\). The pink color indicates values that increase the power consumption forecast \(f(x)\) relative to \(E[f(X)]\), and the blue color indicates values that decrease \(f(x)\). Thus, by adding and subtracting the influence values of the factors from \(E[f(X)]\), the value of \(f(x)\) is obtained. The gray color next to the factor labels shows the values of the factors that influenced the forecast. Features that influenced the power consumption forecast of the second hour 06 February 2020 are shown in Figure 12.  
Figure 11: Actual and forecast power consumption from September 2020 to October 2021. Figure 10: Actual and forecast power consumption from December 2020 to January 2021. The decrease in power consumption forecast of the compressor station (\(f(x)=665.51\) kWh) in relation to the average value (\(E[f(X)]=720.41\) kWh) for the results of LightGBM is justified by  
* Low power consumption 60 h before the forecast hour (1494 kWh);
* The absence of consumption by air-cooling unit 2. The influence of high consumption factors by all GCUs (13,050 kWh) was compensated by relatively low consumption by all ACUs (7430 kWh) and forecast hour (02:00), which is characterized by statistically low power consumption. For the results obtained using XGBoost, the decrease in power consumption of the second hour (\(f(x)=634.65\) kWh) in relation to the average value (\(E[f(X)]=720.40\) kWh) is justified by  
* The forecast hour (02:00), which is characterized by statistically low power consumption;
* Low power consumption 48 h before the forecast hour (818 kWh). The influence of high consumption factors by all GCUs (13,050 kWh) was compensated by relatively low consumption by all ACUs (7430 kWh) and the value of consumption 60 h before the forecast hour (1494 kWh). Figure 12: Influence of the features on the forecast of power consumption of 2 nd hour on 6 February 2020 obtained by (**a**) LightGBM; (**b**) XGBoost. Despite the difference in model construction, the same features had the largest weight in the decision-making process for both models. The features that had influenced the forecast for midnight on 03 April 2020 are shown in Figure 13.  
For the results obtained using LightGBM, the decrease in power consumption forecast (\(f(x)=635.98\) kWh) in relation to the average value is mainly explained by the forecast hour (00:00), which is characterized by statistically low power consumption. Other factors were compensated for by each other. For the results obtained using XGBoost, the decrease in power consumption (\(f(x)=608.30\) xBr\(\cdot\)u) in relation to the average value is explained by low power consumption 48 and 60 h before the forecast hour (818 kWh). Other factors were compensated for by each other. Despite the differences in model construction, it can be noted that both models assigned the highest weight to the same features when making decisions. The overall feature importance is presented in Table 9. It can be noticed that, despite XGBoost making decisions mainly based on the daily power consumption of ACU, other factors can have an influence on certain forecast hours. The same conclusion can be made for LightGBM. Thus, the usage of the SHAP algorithm improves the interpretability of results, resulting in greater trust by experts in training results for machine learning models.