Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a task which is traditionally carried out using deterministic models which rely on data measured through money- and time-consuming methods. Using four basic (and cheap to measure) features (train speed, acceleration, track slope and radius of curvature) from MetroValencia (Spain), we predicted the traction power using different machine learning models, obtaining that a random forest model outperforms other approaches in such task. The results show the possibility of using basic features to predict the traction power in a metropolitan railway line, and the chance of using this model as a tool to assess different strategies in order to increase the energy efficiency in these lines.

This paper presents the training of a neural network using consumption data measured in the underground network of Valencia (Spain), with the objective of estimating the energy consumption of the systems. After the calibration and validation of the neural network using part of the gathered consumption data, the results obtained show that the neural network is capable of predicting power consumption with high accuracy. Once fully trained, the network can be used to study the energy consumption of a metro system and for testing the hypothetical operation scenarios.