## 3 Results and Discussion  
A fragment of the generated database for forecasting the generation of solar power plants, obtained as a result of collecting and processing data from weather providers and data on the generation of SPPs, is presented in Table 2. The dimension of the data after removing outliers and clearing the data from non-numeric values and omissions amounted to 5686 rows and 10 columns. To divide the source data into clusters in order to increase the accuracy of predicting SPP generation, a set of features was generated. This set consists of the following features from the initial database (Table 2) characterizing meteorological conditions:  
* Temperature, actual, \({}^{\circ}\)C;
* Humidity, actual, \(\%\);
* Wind speed, actual, m/s. The choice of features is determined not only by their explicit connection to the meteorological conditions but also by the optimal identification of clusters in the data. Thus, the data dimension for creating a clustering model was 5686 rows and three columns. A fragment of the data used for data clustering is presented in Table 3. To select the final clustering model and the optimal number of clusters, four models were compared, namely KMeansClustering, AgglomerativeClustering, SpectralClustering, and GaussianMixture, and various options for the number of allocated clusters were also considered. For convenient visualization, the PCA transformation was applied to the data. For the data from Table 2, two principal components were allocated to project the data onto a two-dimensional plane. As a result of transforming the data into two-dimensional space, their representation was obtained, as shown in Figure 3.  
Before applying the selected models for clustering, the data from Table 2 were normalized using a minimax transformation (the data in each column was linearly transformed to range from 0 to 1). Visualization of the results of identifying different numbers of clusters by the considered models is presented in Figure 4, and the values of metrics for each model that determine the optimality of cluster selection are presented in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Date** & **Day** & **Solar Angle** & **Time** & **Cloud, p.u.** & **Tem\({}_{\
u}\)\({}^{\circ}\)C** & **Humid.,** & **Wind Speed, m/s** & **Generation, kWh** \\ \hline
26 September 2017 15:00 & 269 & \(-2.21\) & 15 & 0 & 18.0 & 30.0 & 6.944 & 10206 \\ \hline
26 September 2017 16:00 & 269 & \(-2.21\) & 16 & 0 & 18.0 & 30.0 & 6.944 & 8143.8 \\ \hline
26 September 2017 17:00 & 269 & \(-2.21\) & 17 & 0 & 18.0 & 30.0 & 6.944 & 5238.24 \\ \hline
26 September 2017 18:00 & 269 & \(-2.21\) & 18 & 0 & 17.5 & 31.0 & 6.528 & 1984.08 \\ \hline
26 September 2017 19:00 & 269 & \(-2.61\) & 19 & 0 & 16.7 & 32.5 & 5.556 & 141.96 \\ \hline
27 September 2017 07:00 & 270 & \(-2.61\) & 7 & 0 & 7.7 & 63.0 & 4.722 & 35.28 \\ \hline
27 September 2017 08:00 & 270 & \(-2.61\) & 8 & 0 & 7.0 & 66.0 & 4.167 & 1440.6 \\ \hline
26 September 2017 09:00 & 270 & \(-2.61\) & 9 & 0 & 7.2 & 65.0 & 5.000 & 4627.56 \\ \hline
27 September 2017 10:00 & 270 & \(-2.61\) & 10 & 0 & 8.5 & 60.0 & 5.000 & 7786.8 \\ \hline
27 September 2017 11:00 & 270 & \(-2.61\) & 11 & 0 & 10.5 & 52.2 & 5.278 & 9938.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fragment of the database used to predict the generation of solar power plants. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature, Fact, \({}^{\circ}\)C** & **Humidity, Fact, \%** & **Wind Speed, Fact, m/s** \\ \hline
18.0 & 30.0 & 6.944 \\ \hline
17.5 & 31.0 & 6.527 \\ \hline
16.75 & 32.5 & 5.555 \\ \hline
7.75 & 63.0 & 4.722 \\ \hline
7.0 & 66.0 & 4.166 \\ \hline
7.25 & 65.0 & 5.0 \\ \hline
8.5 Figure 3: Data for clustering after transformation into two-dimensional space using the PCA.