## 4 Discussion  
One of the highlights of this work is the use and comparison of different kinds of QM calibrations. Twelve of them were performed with cross-validation, and all of them were successful in reducing the error between ERA5's and the measured wind speeds. This is consistent with the previous usage of this technique in the contexts of energy potential estimation and sensor calibration. One of the findings concerning QM is the similarity between ERA5 grid points. That can be seen in the correlations and error metrics presented in Section 3.1, which indicate that the grid points' wind speed series do not have meaningful differences between them, and that the calibration further homogenizes them. We deduce that taking into account multiple grid points does not add meaningful information to the analysis in terms of predicting the wind speed. This is important for achieving cost-effectiveness because using a single grid point to predict the wind speed module simplifies greatly the wind resource assessment process. It also means that the current ERA5's spatial resolution is high enough for the method presented here to be used in any building, independently of its distance to the closest grid point. Another important finding concerning QM is the increase in the predictive capacity when training and calibrating separately different subsets of data. Seasonal categorization for QM has already been used in the field of meteorology [37]. Regarding directional categorization, one of the authors already used it for QM in the context of wave energy [14], and [PERSON] [38] uses it to remove bias in wind roses. However, as far as we know, this is the first time that directional categorization was used for QM in the context of wind energy, as well as the first time a combined categorization was used for wind energy estimation. The advantage of categorized QM can be seen in the error metrics of the calibrations done on a single point without filtering by wind direction. All of them improve when compared to the uncategorized or simple QM, with just one exception: the directional calibration performs worse than the simple one in terms of the ME. Its negative value suggests that the directional calibration underestimates the wind speed. The PDFs reinforce this; they show that all calibrations get a Weibull distribution almost identical to that of the measured data, with the exception of the directional calibration, which predicts sensibly lower wind speed values. Lastly, the Taylor diagram also shows that the directional QM stands out from the rest. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wind Series** & **Rated Power [W/m\({}^{2}\)]** & **AEP [Kwh/m\({}^{2}\)]** & **CF [\%]** \\ \hline \multirow{4}{*}{Anem over 1 year} & 100 & 51.97 & 5.93 \\  & 150 & 59 & 4.49 \\  & 200 & 63.94 & 3.65 \\  & 250 & 67.55 & 3.08 \\ \hline \multirow{4}{*}{QM dir over 1 year} & 100 & 51.77 & 5.91 \\  & 150 & 58.96 & 4.49 \\  & 200 & 63.28 & 3.61 \\  & 250 & 65.69 & 3 \\ \hline \multirow{4}{*}{QM dir over 10 years} & 100 & 59.59 & 6.8 \\  & 150 & 65.4 & 4.98 \\ \cline{1-1}  & 200 & 68.18 & 3.89 \\ \cline{1-1}  & 250 & 69.77 & 3.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: AEP and CF values for various wind time series and rated powers for the scenario considered as the most realistic. When filtering by wind direction, the results are harder to interpret. First of all, the fact that the calibration's and the measurements' filtering are done with different wind direction series (predicted for the calibrations and measured for the measured wind speed) means that the data points used for each one are not exactly the same, so there is no way to get a Taylor diagram or error metrics. However, there is still the possibility to compare each wind series' PDF. According to them, the seasonal and, especially, the simple calibrations overestimate the wind speed, while the combined and, especially, the directional underestimate it. However, in these cases, there does not seem to be a clear distinction between the different calibrations.