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According to the records of Hong Kong rail operator, MTR Corporation, the weekly electrical consumption of each railway station ranges from 18MWh to 230MWh. Since the electrical consumption of stations is a major factor in the planning of infrastructure, a good prediction of the electrical consumption will greatly assist in the design of the station infrastructure. This study develops an intelligent approach to predict the energy consumption of railway stations. Multi-layered Perceptron (MLP) is adopted to mimic the non-linear correlation between energy consumption, the spatial design of the station, meteorological factors and also the usage of the 19 stations selected. Coefficient of correlation is obtained between the MLP predicted results and the actual collected data to evaluate the performance of the prediction. We apply statistical approach to assess the performance of the developed MLP model. It shows that …