## 4 Photovoltaic Power Plants Generation Short-Term Forecast Operational Correction  
### General Approach to the PVPP Generation Operational Forecasting Models  
When addressing the problem of PVPP short-term forecasting accuracy improvement, it should be outlined that there are several fundamentally different degrees of latitude. Typically, at first, the investigators justify the particular types and parameters of the forecasting approach. The next step is often addressing the data analytics issues, including Feature Engineering, applying practical knowledge to the dataset processing, data gaps elimination, outlier filtration, etc. The last, but not the least, direction to minimize the PVPP generation forecasting error is an adjustment of time resolution of the model. Indeed, it is naturally evident, that very-short term forecasts of PVPP generation demonstrate more accurate results for intra-hour or intra-day periods than multiple day-ahead forecasting models, based on numerical weather predictions. However, typically, the investigators address the behavior of their approaches for static time-domain models, which is justified in the majority of cases by the necessity to introduce another mathematical basis for different time-domains or other structures of the mathematical core of the proposed models, other features with different time resolutions, etc. The static time-domain operational forecasts of PVPP generation for an hour ahead perspective often turns out to be problematic, since the hourly interval is founded to be too large for the models with smaller time resolution (1-min, 5-min, 15-min, etc.) as far as one hour-ahead calculations in such circumstances are to be treated as 60, 12, 4 periods ahead forecasts, respectively. For this reason, the correlation between the PVPP generation at two adjacent hourly intervals is often poorly traced. Furthermore, vice versa, using single hour resolution models for hour-ahead perspective does meet the requirements of the power system operational control since the intra-hour deviations of PVPP generation are not taken into account. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Without Filtration** & **Simple Filter** & **K-Means Method** \\ \hline \(W_{\Sigma}\), kW\(\cdot\)h & 2,235,930.48 & 2,235,930.48 & 2,235,930.48 \\ \(E_{\Sigma}\), kW\(\cdot\)h & 761,973.46 & 640,411.96 & 417,287.68 \\ \(E_{avg}\), kW\(\cdot\)h & 1339.14 & 1151.29 & 733.37 \\ \(\sigma_{E}\), kW\(\cdot\)h & 1909.59 & 2242.59 & 1214.58 \\ \(E_{\Sigma}^{\%}\), \% & 34.08 & 28.64 & 18.66 \\ \(R^{2}\) & 0.65 & 0.70 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the short-term forecasting (STF) results for various filtration models. In the present study, the authors have attempted to establish the bridge between the STF day-ahead forecasting system, implemented on multiple regression with k-means initial dataset filtration, with the operational hour-ahead PVPP generation forecast. The latter one is implemented on the basis of the supplementary STF error forecasting function, giving the opportunity to evaluate the STF error for the hour-ahead time horizon. The knowledge on what would be the mismatch of the STF for the hour-ahead perspective gives the opportunity to implement an operational (very-short term) forecast on the basis of the initially developed and optimized STF approach by providing STF forecasting error correction. The proposed approach is justified by another fact that PVPP generation STF error, if it occurs, exists for several time intervals straight, that is, several hours. This circumstance initiates using retrospective STF error data to make operational forecasts, since the STF errors time series turns out to be more predictable than of the PVPP operational forecast one because of the data noise, appearing in smaller time-domain models. The object of study for operational forecasting is the same PVPP, as was investigated for the STF. Within this study, different methodologies of calculating the forecast for 1 h ahead are considered.