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 calibration and validation of the neural network using part of the consumption data gathered, 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 hypothetical operation scenarios. Keywords Gradient; energy consumption; artificial neural networks; Metro; railway; track layout. 1. INTRODUCTION The transport sector contributes greatly to global energy consumption. According to the International Energy Agency [1], overall energy consumption in 2013 was 2 563.52 Million Tonnes of Oil Equivalent (Mtoe), with the transport sector being responsible of up to 27.6%. Railways are generally much more efficient than road transport in terms of energy consumption for both freight and passengers [2], [3], [4]. Despite this, it is still necessary to reduce their energy consumption in order to improve their competitiveness and contribute to a more sustainable world. For this reason, many strategies are implemented to reduce energy consumption in railways. There are strategies proposed concerning line design, rolling stock and operation [5]. Traditionally, energy consumption of an electric train is monitored at the substations. This provides information about the total energy consumed in an instant, or during a given period of time. However, substations do not give information on how the energy is consumed by each element and subsystem of the railway system, and thus it is not possible to know in detail the impact of any action taken to reduce energy consumption. The current energy consumption in railways depends on many factors such as gradients, maximum speeds, loads, patterns of stops, electrical efficiency of train and power supply system, running resistance, driving style, etc. Researchers have estimated the energy consumption and explored improvements in rail transport through track layout optimisation by means of Geographic Information Systems (GIS) [6], [7]. Other authors have used genetic algorithms to optimise different aspects such as track alignments and operator and user costs for rail operation [6], [7], [8] or crew scheduling [9], [10]. There are methods that aim to optimise travel time and coasting points by using models based on artificial neural networks and genetic algorithms [11]. But these methods do not include gradient or real time measured energy consumption as data.