## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model/Metric** & **R\({}^{2}\), p.u.** & **MSE, kWh\({}^{2}\)** & **MAE, kWh** & **nMAE, kWh** & **ME, kWh** \\ \hline Linear & 0.144 & 3.72 \(\times\) 10\({}^{6}\) & 1300.56 & 97.484 & 8283.6 \\ Regression & & & & & \\ \hline Decision Tree & & & & & \\ Regression & & & & & \\ \hline Random Forest & & & & & \\ Regression & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Forecasting results (trained on initial data). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Initial Data** & **First Cluster** & **Second Cluster** & **Third Cluster** \\ \hline X\_train & 1600 & 1696 & 1581 & 1270 \\ \hline y\_train & 1600 & 1696 & 1581 & 1270 \\ \hline X\_test & 380 & 424 & 396 & 318 \\ \hline y\_test & 380 & 424 & 396 & 318 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Structure of training and test samples for the four datasets. Figure 5: Forecasting results (training on initial data). (**a**)—Linear Regression; (**b**)—Decision Tree; (**c**)—Random Forest. Blue line is the actual data, red line is the forecasting results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model/Metric** & **R2**, p.u.** & **MSE, kWh2** & **MAE, kWh** & **nMAE, kWh** & **ME, kWh** \\ \hline Linear & & & & & \\ Regression & 0.411 & 1.06 \(\times\) 10\({}^{6}\) & 2641.72 & 54.73 & 10,508.132 \\ \hline Decision Tree & & & & & \\ Regression & 0.901 & 1.79 \(\times\) 10\({}^{6}\) & 748.19 & 0.24 & 6788.88 \\ \hline Random Forest & & & & & \\ Regression & 0.931 & 1.26 \(\times\) 10\({}^{6}\) & 637.94 & 0.501 & 5544.294 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Forecasting results (trained on data from the first cluster). Figure 6: Forecasting results (training on data from the first cluster). (**a**)—Linear Regression; (**b**)—Decision Tree; (**c**)—Random Forest. Blue line is the actual data, red line is the forecasting results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model/Metric** & **R2, p.u.** & **MSE, kWh2** & **MAE, kWh** & **nMAE, kWh** & **ME, kWh** \\ \hline Linear Regression & 0.411 & 1.06 \(\times\) 10\({}^{6}\) & 2641.72 & 54.73 & 10,508.132 \\ \hline Decision Tree Regression & 0.901 & 1.79 \(\times\) 10\({}^{6}\) & 748.19 & 0.24 & 6788.88 \\ \hline Random Forest Regression & 0.931 & 1.26 \(\times\) 10\({}^{6}\) & 637.94 & 0.501 & 5544.294 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Forecasting results (trained on data from the second cluster). Figure 7: Forecasting results (training on data from the second cluster). (**a**)—Linear Regression; (**b**)—Decision Tree; (**c**)—Random Forest. Blue line is the actual data, red line is the forecasting results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model/Metric** & **R\({}^{2}\), p.u.** & **MSE, kWh\({}^{2}\)** & **MAE, kWh** & **nMAE, kWh** & **ME, kWh** \\ \hline Linear & & & & & \\ Regression & 0.315 & 9.72 \(\times\) 10\({}^{6}\) & 2624.806 & 59.792 & 9731.67 \\ \hline Decision Tree & & & & & \\ Regression & 0.654 & 4.91 \(\times\) 10\({}^{6}\) & 1434.97 & 0.738 & 10,083.36 \\ \hline Random Forest & & & & & \\ Regression & 0.828 & 2.43 \(\times\) 10\({}^{6}\) & 1053.302 & 1.946 & 6357.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Forecasting results (trained on data from the third cluster). Training separate models for each of the selected clusters and using them together instead of one common model trained on all data will lead to increased accuracy in predicting SPP generation. In this case, the accuracy of the final forecast may decrease due to a decrease in the size of the training sample for each model. This negative impact will disappear with a subsequent increase in retrospective data for the forecasting system. The results of forecasting two models, general and composite (from three models), and their comparison are presented in Figures 9 and 10 and Table 10.  
Figure 8: Forecasting results (training on data from the third cluster). (**a**)—Linear Regression; (**b**)—Decision Tree; (**c**)—Random Forest.