## 4 Discussion
As shown in Figure 8, in the training step the effect of the value of \(H\) is not noticeable, but in the test step we can observe how 60-min aggregation always outperforms the original 5-min time window, specially for WT#81, WT#82, and WT#83. For this specific experiment, \(H=60\) seems to provide even better results than \(H=40\), but checking the numbers in detail, available in Table 4-7 (for \(H=40\), \(H=30\), \(H=50\), and \(H=60\), respectively), we can confirm that \(H=40\) is the only one in which the 60-min aggregation is always the best one in the test phase. The results for \(H=50\) and \(H=60\) have one case in which 30-min aggregation is better than 60-min aggregation. Finally, for \(H=30\) the results are all better for 60-min aggregation but they are worse when compared with \(H=40\). In [4], the ELM paradigm was validated by addressing the problem of the optimal size of the SLFN network. Given the simplicity of SLFN, its architecture only depends on the number of hidden nodes \(H\) (and to a much lesser extent on the activation function considered). As detailed in [4], the accuracy in the training phase is increased when \(H\) is  
Figure 9: Regression lines obtained in the training step for the WT#81 model using 5-min data and (**left**) 60-min data (**right**). In both cases, a SLFN network of \(H=40\) hidden nodes is trained to build the model. increased, but in the test phase there is a value of \(H\) at which the accuracy starts to stabilise and the improvements are no longer significant. In fact, from this stabilisation point, there is a wide range of \(H\) values that provide good performance in the test phase. It is worth noting that the optimal measure of \(H\), the value in which the best accuracy is obtained in the test phase, varies slightly for each WT. Beyond these values, if the number of nodes continue to increase, the test results will worsen due to an over-fitting problem in the training. As shown in the experiment, the network starts to work well from 40 hidden nodes onward. The improvements in the test phase obtained by using 50 and 60 nodes are very small, although still appreciable. Finally, and with regard to the third experiment (Section 3.3), it confirms that not using a signal as an input variable highly correlated with the target when estimating it (the temperature _wrtm_avg_TrmTmp_Brg2_ in the case of our target temperature _wrtm_avg_TrmTmp_Brg1_), which makes it more difficult to predict the target temperature. However, more importantly is that despite the reduction on the performance, it is again observed that the models trained with 60-min data are the ones that perform better in terms of RMSE, both in the training and in the test step. While it has been already proved in [4] that the regression models are able to detect the deterioration of the systems and in a quasi identical way as in [15], in the experimental results it is shown that averaging the SCADA system results beyond the default 5-min data provided by modern WTs, which allows for models that better fit the data. This happens persistently when calculating the projections over a longer period of time and for all the experiments, based on the signal representation described, for time scales of 5, 10, 15, 30, and 60 min. Although it is likely that some of the improvements in prognosis systems that will be seen in the near future will come from the possibility of using high-frequency SCADA data, as that will prevent the loss of information on some of the most important variables in the WT systems, what is currently happening is that there are a large number of wind farms in operation around the world that need to be properly operated and maintained in order to be as competitive as possible to have a long useful life. Most of these parks operate on SCADA systems that perform 5-min prognosis, or even 10-min prognosis in the  
Figure 10: Regression lines obtained in the test step for the WT#81 model using 5-min data and (**left**) 60-min data (**right**).