Research on Dynamic Relationship between Urban Rail Transit and Power Consumption. Issue 1 (May 2020) - Zhe, Liu; Fengqing, Du; Xu, Liu
Abstract: Aiming at the problem of insufficient feature quantity and lack of correlation between feature quantity and load, based on time series analysis method, ARIMNA model is used to fit the load forecasting problem. The study found that the urban orbit data has a good autocorrelation. Compared to the Linear Regression method, when the ARIMA model is used, the algorithm can predict the trend of urban rail transit load data better.