2.4.1. Training and Testing Scenarios

Three different scenarios, with combinations of the two data sources, are used in this study. For the first one, the ANN model is both trained and tested using weather input data from the monitoring dataset. For the second scenario, the model uses the same training with measured data as the first one, but the testing is done using weather inputs from the GDAS dataset. For the third scenario, both training and testing are done using GDAS weather input data. PV power outputs from the monitoring dataset are used in the training stages for all three scenarios. A schematic of the different scenarios is presented in Figure 2.

**Figure 2.** Training and testing scenarios used on the study.

The differences between the first and second scenarios lie exclusively in the testing part of the method. Their ANN models are trained using the same input data, following the same chain of operations, and have virtually the same values on their weight matrices. The results from the first scenario provide a way of evaluating the ability of the ANN to model the studied photovoltaic system. The predictions done by the model of the second scenario are affected by both the nature of the GDAS data and the modelling of the photovoltaic system done by the neural network. Hence, a comparison between the predictions of the first and second scenarios enables an independent analysis of the effects of the GDAS data. The third scenario assumes that no local measured weather data are available, and is able to determine whether the GDAS dataset is adequate to entirely replace these local measurements in the weather data.
