Urban rail energy consumption anomaly value detection method based on time series characteristics
ZHANG Chengxi
XUN Jing
JI ZhiHui
XIE LiJun
FU MinXue
Abstract：To address the limitations of existing urban rail transit energy consumption anomaly detec-tion methods in terms of accuracy and adaptability,this study proposes a subway traction energy con-sumption anomaly detection framework based on time series characteristics.First,a hybrid prediction model is constructed by integrating Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM)networks,and Extreme Gradient Boosting(XGBoost)with an attention mechanism.This approach enhances the prediction accuracy of typical energy consumption values through feature extraction and a nonlinear ensemble strategy.Second,a joint anomaly detection method combining a Density-Based Spatial Clustering of Applications with Noise algorithm and the Local Outlier Factor is designed to enable dynamic threshold calibration and quantitative evaluation of anomaly factors.Fi-nally,the model's performance is validated using real-world data from a subway line.The results indicate that proposed hybrid model reduces prediction error by 47.3％compared to the traditional LSTM,with the Mean Absolute Percentage Error stabilized at 1.02％.The proposed anomaly detec-tion module achieves an accuracy of 96.8％,outperforming the Isolation Forest algorithm by 12.5％.Moreover,the proposed method demonstrates robustness against sudden fluctuations in streaming data.The research results offer a practical reference for anomaly localization and energy-saving optimi-zation in subway energy management,effectively supporting refined energy efficiency monitoring and control in rail transit systems.
Keywords：urban rail transittime Seriestraction energy consumptiontypical valueanomaly detection
Publication Date：2025-06-30
Online Publishing Date：2025-08-21(First online date of this platform, not the publication date of the document)
Pages：10( 120-129 )