## 1 Introduction
The load was represented by an electrical locomotive that passes through the substation, the measuring point of a train. As a result of the measurements, one can see how the power quality is affected by an electric locomotive train moving through the station. We have performed such studies in the past [20; 21], and we found that the power quality is affected in the substation. After analysing the results of the measurements, we conducted the reactive power modelling, which can propose solutions to these problems. Based on these measurements, a reactive power model and a prediction were made. Systems modelling is important in all areas, because with a well-defined model, researchers will be able to better understand and predict the behaviour of a system [9]. Complex systems are more difficult to model because they are characterized by nonlinearities, and it is difficult to obtain a model that characterizes such a system [18; 22]. Prediction of time series has an important theoretical significance, and there are many applications of this type in engineering [23; 24]. Forecasting can be a solution in applications when estimation is not possible [25]; it consists of finding future values of time series data based only on past data [24; 26]. The time series forecast is a current research topic because it has wide applications in many different fields [27; 28]. In modelling time series, some data can be characterized by linear models, and other data must be characterized by nonlinear models [27]. Linear and/or nonlinear systems can be modelled using different methods. For the prediction of time series data, the study [23] proposes a hybrid model that combines a linear regression model and a deep belief network model. In order to make a short-term prediction, paper [24] proposes an alternative integrated model that is based on the improved empirical mode decomposition, autoregressive moving average with exogenous terms (ARMAX), and adaptive network-based fuzzy inference system (ANFIS). For time series, there are also various forecasting methods. ANN-based methods have been proved to work well in nonlinear time series forecasting. The article [28] suggests three prediction techniques that can recognize the hidden connections between inputs and outputs, including artificial neural networks (ANN), deep neural networks (DNN), and models based on long- and short-term memories (LSTM). An efficient strategy to choose the number of clusters and their initial values for fuzzy modelling is presented in the study [29]. Forecasting power load is performed using chaos theory in the study [30]. A convolutional neural network is applied in [31] for prediction. ANFIS is used for modelling complex systems in studies [32; 33; 34; 35]. For short-term forecasting, artificial neural networks (ANN) and hybrid time-series models were employed [36]. The short-term forecasting problem is addressed also in [37] using a method that combines wavelet transform, adaptive evolutionary algorithm, and fuzzy system with GNN. The study [38] proposes an effective method for forecasting short-term load using artificial neural networks. Short-term prediction approaches available in the literature can be divided into two main categories: statistical methods and artificial intelligence-based methods. Artificial intelligence includes artificial neural networks [34; 38], fuzzy inference systems [32; 33; 37] and expert systems [39; 40]. In the statistical methods category are ARIMAX [35; 37] support vector regression (SVR) [41; 42; 43], and stochastic time series [26; 34]. ANN-based techniques are a non-linear modelling method and an efficient predictive technique. It has been demonstrated in many studies that ANNs can achieve the desired accuracy in prediction of nonlinear time series. In fact, there are thousands of published studies using ANN to model nonlinear systems and predict time series. During the last 10 to 20 years, ANN-based modelling techniques have been intensively developed [44].