This paper presents the application of time-series and artificial neural network for improvement of energy forecasting in rail transport section. An integrated artificial neural network (ANN) model is presented that uses autocorrelation and partial autocorrelation functions to determine the best input variables for ANN. The proposed ANN uses autocorrelation function (ACF) and partial autocorrelation function (PACF
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