## 3 Data Filtering for Short-Term Forecasting of Photovoltaic Power Plants Generation
### Data Filtration Methods Application  
One of the possible ways to improve the accuracy of calculating the transparency index can be the data sample filtering, which is used to calculate the coefficients of the regression model [40].  
In order to study the possibility and evaluate the effectiveness of methods for improving the PVPP generation STF accuracy, a reference case study without data filtration is described in detail (Table 2, Figure 3).  
Table 2 represents the numerical assessment of the forecasting quality without introducing data filtration approaches. The following denominations are used: \(W_{\Sigma}\) is the total PVPP energy production for the period under consideration, (kWh); \(E_{\Sigma}\) is the total absolute error for the period under consideration, (kWh); \(E_{avg}\) is the mean absolute error for the period under consideration, (kWh); \(\sigma_{E}\) is the absolute error standard deviation, (kWh); \(R^{2}\) score is the determination coefficient, (p.u.); \(SSEn\) is normalized sum of errors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **26 February** \\ **2018–11 March** \\ **2018** \\ \end{tabular} & \begin{tabular}{c} **21 May 2018–11** \\ **June 2018** \\ \end{tabular} & \begin{tabular}{c} **11 September** \\ **2018–20 September** \\ **2018** \\ \end{tabular} & \
\begin{tabular}{c} **28 January** \\ **2019–3** \\ **February 2019** \\ \end{tabular} &
\begin{tabular}{c} **For All** \\ **Periods** \\ \end{tabular} \\ \hline \(W_{\Sigma}\), kW:h & 391,805.4 & 1,156,028.2 & 601,402.2 & 86,694.7 & 2,235,930.5 \\ \(E_{\Sigma}\), kW:h & 185,497.2 & 253,367.0 & 258,279.6 & 64,829.7 & 761,973.5 \\ \(E_{avg}\), kW:h & 1131.1 & 1306.0 & 1986.8 & 800.4 & 1339.1 \\ \(\sigma_{E}\), kW:h & 1909.6 & 2201.9 & 2040.6 & 1303.4 & 2210.2 \\ \(R^{2}\) & & & & & 0.65 \\ \(SSEn\) & & & & & 14.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of PVPP generation forecasting accuracy.  
Due to the fact that when calculating \(SSE\) (sum of square error), very large values are obtained (for example, for the Figure 3 scenario the \(SSE\) would be equal to 3,322,661,719.61 kW\({}^{2}\)), the \(SSEn\) indicator is applied. It is calculated as the sum of squared errors normalized with respect to the square of the installed capacity of the photovoltaic power plant, \(SSEn=SSE/(P_{inst})^{2}\), measured in p.u.  
In Figure 3 there is a diagram showing the scatter of the forecasted values of PVPP generation relative to the actual values.  
The determination coefficient characterizes a significant value of the PVPP generation forecasted values relative to the actual values. The reason for this is the cloudiness influence ambiguity on the value forecasted by the regression function--the transparency index.  
Figure 4 shows an example of the transparency index dependence on cloudiness for the solar altitude angles, characterizing the morning and evening conditions (\(\alpha<15^{\circ}\)), as an example, and shows a significant uncertainty of the cloudiness influence \(cc\) on the transparency index \(k_{T}\). For the same cloudiness value, the scatter in the transparency index values can reach 0.9 p.u.