Construction of a Prediction Model for Energy Consumption in Urban Rail Transit Operations Using a Bottom–Up Approach

• Published in: Energies
• Main Authors: Boyu Chen, Ye Lin
• Format: Article
• Language: English
• Published: MDPI AG 2025-02-01
• Subjects: energy conservation and emission reduction; multiple linear regression analysis; random forest regression model; urban rail transit energy consumption
• Online Access: https://www.mdpi.com/1996-1073/18/4/888

Global climate change necessitates an immediate reduction in carbon emissions. This study aimed to categorize rail transit energy consumption factors into “traction energy consumption” and “non-traction comprehensive energy consumption” by employing a bottom–up approach and using a sample of urban rail transit operations in 122 Chinese cities from 2018 to 2022. The factors were grouped based on the scale of the urban rail transit network, and planned indicators were screened using stepwise regression and machine learning eigenvalue methods. Predictive models were then constructed using these planned indicators through multiple linear regression and random forest regression. This process yielded five traction energy consumption prediction models corresponding to different network scales as well as one non-traction comprehensive energy consumption prediction model. The applicability of these models was determined through comparison. Additionally, a direct linear relationship between the planned indicators and urban rail transit energy consumption was established using multiple linear regression. This study provides solid support for accurately predicting the energy consumption of urban rail transit operations and optimizing resource allocation. It offers valuable insights for carbon accounting and related research endeavors.