## 7 Conclusions  
The present study addresses the development of short-term and operational forecasting methods of photovoltaic power plants, which arose in response to the energy sector transition, characterized by the growing share of stochastic power generation. As it was stated in the introduction, reliable forecasting systems are the most effective and least capital-intensive measures that allow for the integration of stochastic generation sources into the power systems. Based on the results of the previously carried out investigations, a short-term day-ahead PVPP generation forecasting system was developed by an effective combination of astronomical and statistical approaches. The step-by-step forecasting procedure, comprising the global horizontal irradiance identification, tilted surface irradiance assessment and, finally, calculation of the PV power plant output was implemented, providing rich opportunities for forecasting results' interpretation and overall model flexibility, allowing us to take into account the operational state of the power generation equipment, switchgear composition, operation modes of the adjacent power system and other external factors. In the course of the short-term forecasting system industrial piloting, it was determined that the greatest error (more than 70% of the total value of mean absolute percentage error) in the PVPP generation forecast calculation is introduced at the stage of determining the transparency index according to the regression model due to the ambiguity of cloudiness impact on the forecasted value of the transparency index. Given that from the point of view of PVPP power output, there is a fundamental difference of various meteorological conditions and events, which may be characterized by similar weather forecasting parameters, acquired from the open-source weather data provider, it was decided to focus the attention on the dataset filtration. The idea was to eliminate the dataset outliers and to use separate training sets for various weather conditions and/or seasons to enhance the \"sensibility\" of the model to the weather type. The results of studying the possibility of filtering the initial data to improve the PVPP generation short-term forecast accuracy allow us to conclude that the application of the k-means methodology is the most effective. The PVPP generation forecast error is reduced by almost 2 times compared with the calculation without filtering and more than 1.5 times compared with the calculation using an empirical filter. The mean absolute percentage error for PVPP day-ahead forecasting with k-means data filtration was calculated to be 18.66%. Thus, it can therefore be concluded that the use of the k-means method for filtering the initial data allows for reducing the PVPP generation forecast error introduced at the stage of forecasting the transparency index to obtain a more accurate forecasting result. Subsequent improvement of PVPP forecasting accuracy was achieved by adjusting the time-domain of the model by adding the intra-day forecasting procedure, realized in the form of forecasting error prediction. It was assumed that the knowledge of present-day forecasted PVPP generation and the mismatch of the forecasted values compared to the actual data, acquired from irradiance and electrical meters, will give us the opportunity to develop a powerful multi-time-domain forecasting tool. Unlike the other existing approaches, the authors have attempted to establish the bridge between the day-ahead PVPP forecast and the operational hour-ahead PVPP generation forecast by introducing an error forecasting procedure. We provided the comparison of the persistence model, moving average, autoregression, autoregressive moving average and autoregression with exogenous features for short-term forecast error prediction. It was found that the second-order autoregression model AR(2) for short-term forecasting error prediction outperformed all of the methods under consideration.