Open AccessResearch on Dynamic Relationship between Urban Rail Transit and Power Consumption
Author(s) -
Zhe Liu,
Fengming Du,
Liuxiong Xu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/853/1/012029
Subject(s) - autocorrelation , autoregressive integrated moving average , partial autocorrelation function , urban rail transit , feature (linguistics) , time series , computer science , series (stratigraphy) , linear regression , regression analysis , power (physics) , power consumption , consumption (sociology) , econometrics , statistics , mathematics , engineering , transport engineering , machine learning , paleontology , social science , linguistics , philosophy , physics , quantum mechanics , sociology , biology
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.