Abstract
Urban rail system is one of the most energy-consuming and highest costing public sectors in cities, and its energy efficiency has great potential to improve on the system level. The aim of this study is to introduce a novel method for identifying key energy-consuming parts based on univariate uncertain data pattern miner (U2P-miner) in order to improve the energy efficiency of urban rail system and to apply it in a real urban rail system. Firstly, an energy consumption association network model is constructed based on the operation energy consumption by analyzing the association among the energy use during operation. Secondly, a method for identifying critical nodes using U2P-miner algorithm is presented, which consider both topological and energy-consuming attributes of nodes. Finally, the proposed method is applied in Beijing Subway to help operator make better choices in energy efficiency improvement.
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