**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 e ffective 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 e ffective 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 di fference 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 e ffective. 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.

The developed approach of operational forecasting makes it possible to reduce the total error by almost 1.5 times in comparison with the short-term forecast. The mean average percentage error was calculated to be about 13%. At the same time, the operational forecast makes it possible to obtain more robust estimates of the PVPP generation predicted values: the percentage of operational forecasts meeting the confidence interval of ±1 MW (for 15 MW PVPP) is more than 88%, and the percentage of the forecasts meeting the confidence interval of ±2 MW is more than 97%. Thus, this confirms the effectiveness of the proposed methodology for an operational forecast based on retrospective data on short-term forecasting errors.

The proposed forecasting software was installed on a low-cost distributed computing system, characterized by robust and maintenance-free operation, which is of grea<sup>t</sup> importance for power generation facilities operated in the autonomous mode and/or providing system service at the wholesale energy market. Moreover, in the course of PVPP operation, the retrospective dataset is being permanently updated with the newly introduced measurements and calculation results. The proposed hardware system has outstanding scaling properties, so there is no need to introduce high-performance computing facilities even for the Big Data sets.

**Author Contributions:** Conceptualization, S.A.E. and D.A.S.; data curation, S.A.E., A.I.K., D.A.S. and A.M.R.; formal analysis, V.V.D. and A.M.R.; funding acquisition, A.I.K.; investigation, S.A.E., D.A.S. and D.N.B.; methodology, S.A.E., A.I.K. and D.A.S.; project administration, A.I.K.; resources, S.A.E., V.V.D. and D.N.B.; software, S.A.E., A.I.K., V.V.D. and A.M.R.; Supervision, S.A.E. and A.I.K.; validation, D.A.S., V.V.D., A.M.R. and D.N.B.; visualization, D.A.S.; writing—original draft, S.A.E., A.I.K. and D.A.S.; writing—review and editing, D.N.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** No funding was received for this study.

**Conflicts of Interest:** The authors declare no conflict of interest.
