A Neural Network Model for Calculating Metro Traction Energy Consumption

Jia FENG, Xia-miao LI, Mei-quan XIE, Li-ping GAO


Due to the difficult of parameters calibration in existing three metro traction energy consumption models, this research first develops a gray related hierarchy analysis model to determine the main factors mainly considering the operational data. Furthermore, a traction energy consumption model based on neural network model is accordingly proposed to calculate the traction energy consumption of metro of one line due to statistics data which are gained by gray related hierarchy analysis model. It is found that the relative error of predicted values and actual values is a maximum of 8.61%, a minimum of 0.01% and the average relative error is 3.12% by using the operation data from one of Beijing subway lines. Results indicate that the model can predict traction energy consumption of a single metro line with high accuracy.


Neural network, Traction energy, Traction energy consumption model, Rail transit


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