## 4 Conclusions  
The study found that training separate models for each of the selected data clusters significantly improves the accuracy of forecasting solar power plant (SPP) generation. It is important to note that the model trained on the original data showed even lower accuracy compared to the model for the second cluster. Thus, to achieve higher results in predicting solar power generation, it is necessary to consider the data volumes and their quality when choosing a suitable model. Thus, the approach with the use of separate models for clusters seems more promising.  
The obtained results show that the composite model provided a reduction in the nMAE of the forecast that was nearly twice that of the general model and a minor improvement in terms of other considered metrics. These improvements were obtained due to data clustering and tuning the individual models for each cluster. The first cluster contains mostly data observations related to the winter and autumn period with medium level humidity (50-70%). The second cluster data are characterized with lower level humidity (below 50%), and there are no winter data. The third cluster contains data related to high-level humidity (close to 100%) and represents rainy or snowy days mostly. Tuning of the individual models helps to target specific features of each cluster better than through one model exclusively. The individual models are less complex than one general model at the same time. This helps to avoid overfitting and to increase the accuracy.  
The general model still provides acceptable results on average and can be used for the territories where the weather is close to stable during the whole year. However, for the territories with changing weather and well-defined climatic seasons, it is recommended to use composite models.  
The main contributions of this research in comparison with other studies are using weather features which describe the surrounding effects of the working state of the PV modules; clustering each observation (one hour resolution) instead of clustering days; and evaluation of clustering results with appropriate metrics (silhouette coefficient, BSS and WSS).  
Additionally, the use of individual models for each cluster may have some disadvantages, such as the training sample size for each model decreasing with number of the clusters increasing and the underperformance of this approach due to the number of clusters being too high or too low. In further research, the negative effects of the proposed approach may be eliminated with the collection of more retrospective data, control of the adequate number of clusters, and exploring additional features that will affect the clustering process positively.  
Conceptualization, A.M.B. and S.A.E.; methodology, K.I.H., A.M.B. and P.V.M.; software, K.I.H. and A.M.B.; validation, A.M.B. and S.A.E.; formal analysis, P.V.M.; investigation, A.M.B. and S.A.E.; writing--original draft preparation, K.I.H.; writing--review and editing, A.M.B. and P.V.M.; visualization, K.I.H.; supervision, S.A.E. and P.V.M. All authors have read and agreed to the published version of the manuscript.  
The research was carried out within the state assignment with the financial support of the Ministry of Science and Higher Education of the Russian Federation (subject No. FEUZ-2022-0030 Development of an intelligent multi-agent system for modeling deeply integrated technological systems in the power industry).  
Dataset available on request from the authors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model\textbackslash\{}Metric** & **R2, p.u.** & **RMSE, kWh** & **MAE, kWh** & **nMAE, kWh** & **ME, kWh** \\ \hline General model & 0.892 & 1340.77 & 829.424 & 7.69 & 6766.846 \\ \hline Composite model & 0.899 & 1297.26 & 800.566 & 4.1 & 7073.089 \\ \hline Changing metrics & 0.007 & 338.79 & 28.26 & 3.59 & \(-\)306.24 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the forecasting results using a general and composite model.