## 4 Discussion
It could be that the directional calibration performs worst when all wind directions are taken into account but that it performs better at the direction with the most data, which is the wind directions facing the facade. That could explain that it goes from being the worst calibration to be on par with the rest when filtering by wind direction. Theoretically, the combined calibration should also have this advantage. It is logical to think that, since both the seasonal and the directional categorization give better results than the lack of categorization, the combined calibration would get the best results. However, that is not true because the directional calibration scores the best AEP results, and the combined one comes second. It may be that such an extensive categorization leaves insufficient data points to properly train the transference functions for the combined calibration. Since combined calibration splits the data according to 4 seasons, as well as the same directions used for the directional one, each combined QM transference function was trained with 4 times less data than the directional QM functions. All the results already discussed seem to suggest that the directional calibration should be one of the worst at predicting the AEP. However, it is the calibration that gets closer to the AEP values calculated with the measured data in most scenarios, with the combined calibration being the one that performs best in the rest of scenarios. It is logical to think that, since both the seasonal and the directional categorization give better results than the lack of categorization, the combined calibration would get the best results. However, it may be that such an extensive categorization leaves insufficient data points to properly train the transference functions for the combined calibration. Since combined calibration splits the data according to 4 seasons, as well as the same directions used for the directional one, each combined QM transference function was trained with 4 times less data than the directional QM functions. Regarding the use of random forest to predict wind direction, the confusion matrix shows that the classification has margin for improvement. However, the wind rose plotted using the predicted wind direction and the calculated AEP values tells us that the classification was good enough for our wind resource purposes. After all, the AEP values for the directional calibration, which depend on the predicted wind direction, have an overall relative error of 6.77%. Our interpretation of this apparent contradiction is that not every classification error has the same negative impact on the energy potential estimation. For example, there is no impact if the classification mixes two wind directions that are both inside the facade's absorption range. Apart from that, the classification seems to mix the winds coming from both sides of the valley; that is, it tends to mix the south-western wind with the north-eastern wind. Even if this kind of error can affect the estimation, it is reasonable to think that these kinds of errors partially compensate each other's negative impact. Finally, a series of AEP and CF estimations were made taking into account various possible rated powers. The results show that the AEP and CF over 10 years are slightly higher than the AEP and CF calculated with both the predicted and the measured wind data. This means that the 1-year period was less windy than the typical year on that location and that, without the calibration process, the potential would have been underestimated. The AEP and CF values are also meant to help select an adequate rated power. When doing so there is a trade-off between the total amount of produced energy during the lifetime of an installation and the investment's payback time. In this case, the low CF values tell us that a low rated power is the most reasonable choice. However, it seems that these low values come from the low measured wind speeds.