*3.3. Comments on Training and Testing Scenarios*

After presenting and commenting on the relevant results for photovoltaic power predictions in the three different training and testing scenarios, a brief discussion about the implications of each scenario is in order.

The first proposed scenario both trains and tests the performance of the chosen ANN model using data from the monitoring dataset. No inaccuracies due to the forecast nature of GDAS data are fed into the neural network model here. Hence, all differences between predictions and actual values are due to the mathematical modelling of the PV system done by the ANN model. As all the tested error metrics have great performance in this scenario, it can be concluded that the proposed neural network model is indeed adequate to model the studied installation.

The second proposed scenario uses the same training as that of the first scenario, and thus, the resulting trained model is virtually identical, and the same conclusion about its modelling performance applies. The testing part here is done using GDAS data. As already stated, differences in the prediction performance between the first and second scenarios can be attributed to uncertainties in the GDAS data. The error values are indeed higher for this second dataset, but relative differences are not drastic. They can still match (and even outmatch) the performance of other forecast strategies reported in the literature, as stated in the previous subsection. For a generic monitored PV installation, this scenario would be translated as having an ANN model trained with a historic dataset of in situ measurements of weather variables. In this case, GDAS data could still be used to predict power production for the next hours based on GDAS forecasts, or to fill missing days in historic datasets.

The third and last scenario completely replaces the weather data from the monitoring dataset with those from the GDAS dataset, keeping only the measured PV power outputs from the former. Here, the trained ANN model differs from that of the other two scenarios (although the same basic configuration is used), so the prior conclusion about the performance of the ANN model does not apply. However, error results for this scenario are most similar to those of the second one, with only slightly worse bias errors and almost identical RMSE and nRMSE errors. This implies that a historical dataset of in situ weather measurements may not be a crucial item when aiming to predict future power outputs with GDAS. Further studies targeting different locations and facilities would be required to confirm this. Nevertheless, it seems that even if the sensors for monitoring relevant weather variables were never available at the location of the PV system, GDAS forecasts could still be used to train an ANN model which is able to predict power production.
