## 2 The Main Sources of the PVPP Generation Forecasting Errors  
Given the step-by-step procedure, introduced in the PVPP generation short-term forecasting algorithm, the total forecasting error of the PVPP generation is the sum of the errors of individual mathematical models, used at each step consequently. Moreover, there are special cases when the error of one of the models compensates for the error of the other one, but there are also scenarios when the errors of the models overlap each other, causing a significant increase in the total error.  
As a rule, at the PVPP, there are measurements of the solar irradiance and the corresponding electrical energy generation at the alternating current side of the group of the inverters. Unfortunately, it is not possible to separately estimate the forecasting error that is introduced at each stage. However, these measurements allow for isolating the error components of the model, associated with tilted irradiance identification and PV panels and inverters outputs calculation (from 3 to 6 stages) and to estimate the total error of the forecasting methodology (from 1 to 6 stages). The calculation procedure uses data on the actual measurements of the solar irradiance acquired from the horizontally installed pyranometers. Figure 2 illustrates the forecasting error of the entire methodology on an hourly basis while using the actual metering data on solar irradiance. It is notable that the forecasted values of the PVPP generation are mainly conditioned by the forecasted values of the cloudiness, which were not observed at the given site. This also brings up an issue of the PVPP forecasting error estimation, including identification of the errors introduced by the data sources.  
Table 1 shows the calculation results for two PVPP forecasting scenarios and presents an estimate of the mean absolute percentage error (MAPE) without taking into account the error of stages 1-2 for the first scenario and taking into account the error of all stages for the second scenario.  
The calculation results presented in Table 1 demonstrate that the main share of the PVPP forecasting error is introduced at the stage of calculating the global horizontal solar irradiance.  
The value of the global horizontal solar irradiance, as well as the transparency index of the atmosphere, is largely determined by the cloudiness. The proportion of solar energy passing through the cloud layer is not constant. It depends on a several factors [40]:  
* the numerical estimation of cloudiness (the proportion of the sky covered by clouds);
* type of clouds (cirrus, cumulus, stratus, etc.);
* cloud heights from the base to the top;
* the microstructure of clouds (e.g., water content per unit volume);
* distribution of clouds relative to the solar disk position, etc.;  
In the majority of studies and for practical applications, given the limited amount of available meteorological data from local data providers, to assess the effect of cloudiness on the value of solar irradiance and the transparency index, as a rule, only one of the listed parameters is used--the cloudiness (in proportions or percentages)--since for this parameter it is the easiest to determine (to observe) and the easiest to forecast [41].  
As a result, a situation arises when the parameter for which the forecast is made has an ambiguous effect on the forecasted value. Examples of such situations are described by the authors in [42; 43]. As a result, at this stage of calculating the PVPP generation forecast, a significant forecasting error is introduced.