## 4 Analyses
### Results
* The typical SCADA resolution, i.e., 600 s, could be problematic for the temperatures of the gearbox shaft bearings, of the gearbox oil, and inside the nacelle, as only 80% of the data had an error below 1 \({}^{\circ}\)C.
* Gearbox shaft bearings and gearbox oil temperatures could occasionally exhibit information losses higher than 2 \({}^{\circ}\)C and more rarely higher than 3 \({}^{\circ}\)C for aggregation periods of 600 s.  
Turbine-Dependency  
The results of the previous calculation of \(\Lambda(t)_{\Delta_{\text{agg}}}\) could also be split between turbines, obtaining a breakdown of the information loss across the whole wind farm, as shown in Figure 7. This analysis allowed us to verify whether information loss was a condition imputable to the behavior of single turbines, or rather a generalized phenomenon affecting all turbines in the farm. Only a selection of temperature signals and resolutions is presented, the complete results table can be found in the Supplementary Materials. The following observations can be drawn:  
* Not a single turbine or a set of turbines was responsible for the entire amount of information loss. * There were variations in the amount of lost information across turbines. For example, observing the percentage of data of the gearbox shaft bearing temperature with an aggregation time of 600 s for which the aggregation error was below 1 \({}^{\circ}\)C, the differences between turbines could vary within a range greater than 20%. In particular turbines WT04 and WT06 had slightly more than 60% of the data in the lowest error bin versus turbine WT02, WT03, and WT11 that had approximately 90% of the data within the 1 \({}^{\circ}\)C error range. * The differences between turbines were principally only visible for transmission related signals, i.e., gearbox oil and gearbox shaft bearing temperatures. For all other temperatures differences between turbines were not noticeable, as information losses were overall very limited. * Aggregating the signal at a lower, yet still coarse time resolution, i.e., 300 s, reduced the differences between turbines. The variation range was closer to 10% in this case. * While shorter aggregation periods reduced the differences between turbines, it did not change the relative impact of information loss within the wind farm. This means that the most affected turbines at 300 s aggregation were also the ones showing greater losses at 600 s.  
Figure 6: Information loss of three exemplary temperature signals for selected aggregation resolutions. Error bins are temperature intervals measured in degrees Celsius. Figure 7: Information loss results subdivided into all wind turbines for the temperatures of Figure 6. Two time resolutions are provided: 300 s in the top row, 600 s in the bottom row. Error bins are temperature intervals measured in degrees Celsius. Seasonality-Dependency  
Furthermore, the results could be divided into subgroups of the month of the year, as the information loss behavior may vary along the seasons due to environmental conditions such as the wind. Figure 8 shows the variation of information loss for temperature signals throughout the year. Each month was assigned a bar, error bins and aggregation resolutions were set as in previous figures. Results for signals and resolutions not included in the figure are provided in the Supplementary Materials. The results show:  
* A seasonal variation in the amount of lost information was visible for gearbox oil and gearbox shaft bearings temperatures. For the 600 s aggregation period, the percentage of data having an error below 1 \({}^{\circ}\)C decreased by approximately 10% between summer and winter months. * The highest losses were registered during the months of November, December, and January when the percentage of data below 1 \({}^{\circ}\)C error was around 70% and 80% for the gearbox shaft bearing 1 and gearbox oil temperature respectively.