Traction energy accounts for a majority of energy consumed by electric multiple unit (EMU) trains. In this study, factors affecting the traction energy consumption of EMU trains are qualitatively and quantitatively analyzed to conserve energy and improve operational efficiency of high-speed railways. First, influencing factors are recognized and categorized into three groups. Then, quantitative analysis is performed using the self-organizing data mining method to analyze the factors affecting traction energy consumption of high-speed EMU trains based on data acquired from a joint debugging and commissioning conducted on EMU trains. The data mining and empirical results are compared for verification, revealing that self-organizing data mining is feasible and efficient to analyze the energy consumption of EMU trains and is better compared to empirical analysis. Because its parameters are dynamic, the self-organizing data mining results can be readily and accurately fit to independent and dependent variables based on the observed data and statistical analysis. Finally, the generality and significance of self-organizing data mining in analyzing the energy consumption of EMU trains considering a large amount of EMU train operation data is discussed.

牵引能量占电动多单元（EMU）列车消耗的大部分能量。在本研究中，定性和定量地分析了影响动车组牵引能耗的因素，以节约能源并提高高速铁路的运营效率。首先，将影响因素识别并分为三类。然后，使用自组织数据挖掘方法进行定量分析，基于对动车组列车进行联合调试和调试获得的数据，分析影响高速动车组列车牵引能耗的因素。比较数据挖掘和实证结果以进行验证，揭示了自组织数据挖掘对于分析动车组的能耗是可行和有效的，并且与经验分析相比更好。由于其参数是动态的，因此基于观察到的数据和统计分析，自组织数据挖掘结果可以轻松，准确地适合自变量和因变量。最后，讨论了考虑大量动车组运行数据的自组织数据挖掘在分析动车组能耗中的一般性和意义。