Granularity Optimization for Efficient Energy Consumption Monitoring in Subway Stations for Enhanced Energy Management

Abstract

Efficient energy data management forms a critical foundation for unlocking the carbon reduction potential of subway systems, holding significant importance in advancing urban processes towards lowcarbon and clean environments. Low-precision sampling is difficult to reflect the actual energy consumption of the station, and high-precision sampling has high requirements for the data storage and transmission capacity of the monitoring system. In order to determine the appropriate sampling accuracy, this study analyses the power load fluctuation characteristics of stations on a subway line in the North China Plain and optimizes the sampling granularity for achieving minimal data storage requirements while effectively capturing energy consumption fluctuation information. The findings indicate that a higher sampling granularity for power load monitoring is advisable during the summer to capture the frequent fluctuation characteristics of ventilation and air-conditioning system energy consumption. For a typical underground station, it is recommended to use a sampling interval of 5 min in summer and 15 min or longer in other seasons. For a typical elevated station, a sampling interval of 10 min is recommended in summer, and 20 min or longer in other seasons.