## 4 Analyses
### Q2: Do External Conditions Have an Effect on Information Loss? Knowing that operating conditions of turbines vary considerably both on short, i.e., hours, and long timescales, i.e., months of the year, an analysis of the relation between information loss and external conditions is presented. As wind speed is one of the most important parameters governing turbine operations, the analysis focuses specifically on this. The objective is to characterize the behavior of the information loss with respect to the wind speed. We answer the questions whether certain wind conditions cause larger deviations in the aggregated signal and, if so, what the expected range of information loss is. Then, the behavior of different  
From an overall perspective it can be noticed that active power, all currents, gearbox oil, and gearbox shaft bearing temperatures were characterized by a quite large mean information loss of 0.12 to 0.2 IQR for wind speeds ranging from 7 to 12 m/s. This range corresponds to the upper part load region of the power curve. Other signals follow that trend but had less pronounced maxima here such as the power factor, the temperature of the generator stator and the voltages. Albeit, the voltages showed another maximum for high wind speeds around 25 m/s. Another group of signals with the rotor and generator speed as well as the temperature within the nacelle were characterized by overall lower average losses and their maxima were located around wind speeds between 5 and 7 m/s just above the typical cut-in wind speeds of wind turbines. The frequency value had the maximum error outside the range of our heat map. However, its red band shows us that most information was lost in the operating region of the turbine from 2.5 up to 20 m/s. The wind speed itself had most of its information lost for high wind speeds. The remaining signals had a much lower span of variation of their information content. Therefore, no critical wind regimes could be easily identified. Overall, Figure 10 clearly shows that there were shared patterns in information loss between signals, but these were not unique. Some signals had larger information losses during the transition toward nominal power conditions, others were more affected at lower wind speeds, others again for high wind speeds, and finally certain signals were barely affected by changes in the range of information loss. A second perspective focuses on the quantification of the the expected range of variation of information loss. Thus, the 5-95 th percentile of the distribution of the signed local error and the mean value were sorted into the same wind bins of 0.5 m/s already used above for different levels of signal aggregation. The results for all available signals are represented in Figure 11. Values of information loss are reported in the native units of the signals without any normalization, allowing for an easy interpretation of the results. The plots further allowed us to visualize the typical profile of information loss with respect to the wind speed. Additionally, it was possible to quantify the extreme range of variation of the information content that can incur as a consequence of aggregating signals. Figure 11 is organized into subplots in which each individual signal has its own subfigure. The wind  
Figure 10: Heat map of the mean average of the aggregation error value per wind speed bin. Signals are normalized by the IQR and the color scale is capped to avoid grid frequency and wind speed to hide the behavior of the other signals. speed is assigned to the x-axis measured in m/s, on the y-axis the values of the signals in their original units are reported. Positive values indicate that the aggregated value is above the raw data, negative values the inverse, respectively. The dashed lines correspond to the values of the 5-95 th percentile for each aggregation level in different colors. Figure 11: Line plots of the range of variation of information loss for the available signals over wind bins of 0.5 m/s width.