## 4 Conclusions  
Analysing the modelling by the ANFIS method, it can be seen that the modelling results depend on the type of architecture used, but not very much. When using the ANFIS grid partition, the ANFIS architecture is highly dependent on the number of previous samples used in modelling. If more than 5 previous samples are used, many rules will result in the rule layer, even for a small number of fuzzy sets in the input layer. Regarding the subtractive clustering method and the FCM method, it can be seen that the results are similar. The difference is that the FCM specifies the number of clusters in advance, while in the subtractive clustering method, the number of clusters results after establishing the value for the influence radius parameter. In any case, in both the subtractive clustering method and FCM method, the training time is not high. That is because there are not many rules in the rule layer.  
It can be said that modelling and prediction with ANFIS works well and that it is useful to better understand how reactive power varies in a power station of electric locomotives. A better understanding of these phenomena also facilitates actions that can be taken to limit the factors that lead to decreased power quality.  
The RMSE value during testing is another indicator that the modelling produced good results. For modelling with the ANFIS grid partition, this value falls within the ranges of 0.012 and 0.015, and for this range, the regression coefficient is between 0.92 and 0.95. Further, the modelling is efficient for ANFIS subtractive clustering, obtaining RMSE between 0.011 and 0.02 during the testing phase. Additionally, the RMSE for modelling with ANFIS FCM is between 0.012 and 0.018. These demonstrations are for configurations that avoid overtraining.  
Considering Tables 1-3, it is possible to say that there are ANFIS configurations in which the phenomenon of overtraining can occur. This happens particularly when too many rules are implemented, which results in good performance during the training stage but limits the system's ability to generalize. Due to the ability to produce numerous rules even for a small number of fuzzy sets in this architecture, the ANFIS grid partition structure is particularly susceptible to the overtraining phenomenon.  
The regression coefficient does not drop below 0.9 for the parameters at which the phenomenon of overtraining does not occur, according to an analysis of all three approaches for ANFIS. This supports the earlier conclusion that using ANFIS, one may achieve an accurate modelling of reactive power variation that is helpful for making short-term predictions.  
Figure 16: Regression coefficients for the training and testing phase, for ANFIS FCM model.  
Future modelling efforts will make use of trendy ANN architectures, such as convolutional ANNs developed on deep learning.  
A general conclusion about modelling with ANFIS, regardless of the chosen architecture, is that it works well. The parameters of the models must be chosen so that the training time does not increase too much because, in addition to this increase in training time, most of the time there is an overtraining phenomenon that causes the network to not work properly during the testing phase and lose its ability to generalize.  
Methodology, M.P.; Software, S.M.; Validation, C.P.; Investigation, M.P. and C.P.; Writing--original draft, M.P.; Writing--review & editing, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.