## 2 Data and Methodology
### Calibrations via Quantile Mapping  
Once the training and testing data sets were ready, the measurements were calibrated against the reanalysis 10-m wind data using quantile mapping, quantile matching or QM. This technique seemed  
Figure 6: Wind rose of the wind measured in the building.  
Figure 7: Representation of the studied building and the relevant information to choose a facade for the installation. The wind rose is the same of Figure 6. The light blue area corresponds to the absorption range of the selected facade. The dotted red line marks the valley’s direction. The green square marks the anemometer’s location on the roof.  
appropriate for energy output estimation purposes because it makes the PDF of the reanalysis data more akin to that of the measurements. It has already been used by the authors in a wider context of renewable energies, such as the study of historical wave energy trends in Ireland, Chile, or the Bay of Biscay [13; 14; 15; 27], and by other researchers in the context of solar [16; 17] and wind energy [18; 19; 20].  
A first round of simple calibrations was performed for each of the ERA5 grid points using quantile mapping. We call them _simple_ because they were not categorized, meaning that the training data set was not splitted, as opposed to the following calibrations. The results, which are more extendedly discussed in Section 3.1, showed that both ERA5's and the calibrated wind speed did not show meaningful differences between grid points. For these reason and for simplicity's sake, only the grid point closest to the anemometer was used for the next three calibrations. These are performed on categorized data sets, meaning that the training data set was split using different criteria and that the training and calibration process were done independently on each split. The first of them was directional, the second one seasonal, and the third one was both directional and seasonal; each one's most distinguishing properties are summarized in Table 2 for clarification purposes.  
To perform the directional calibration, 8 wind directions were used, corresponding to the cardinal and intercardinal directions. All data points were grouped according to which of those directions were closer to their ERA5's wind direction, and separate training and calibrations were done for each one. This way, the calibrations of the data points that had a ERA5 wind direction between established ranges were done using transference functions trained only with data points with a ERA5 direction within that range.  
The seasonal calibration consisted of splitting the data set based on the season instead of the direction. For simplicity, instead of accurately grouping the data points according to seasons, they were grouped by months: the data from January, February, and March was considered as being part of \"winter\"; April, May, and June as \"spring\"; July, August, and September as \"summer\" and October, November, and December as \"autumn\". Following the same logic as the directional calibration, the data from each season was trained and calibrated separately.  
The third and last calibration was a combined calibration, meaning that it was directional and seasonal at the same time. The directions and seasons were the same as in the previous calibrations, so the data points were split in 32 groups, according to their ERA5 direction and season.