Predicting Traction Energy Demand of a Light Rail Vehicle Using Deep Learning Methods

Tesar, Markus; Muller-Broich, Stefan; Hilmer, Lars; Gratzfeld, Peter

doi:10.1109/vppc53923.2021.9699347

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modelling the traction energy demand is a complex issue. Locomotive energy consumption is affected by multiple factors such as the type of locomotive, locomotive routing (train type and running path), tractive weight, transport organization, locomotive quality, weather conditions, and driver's handling [13], [14]. So locomotive traction energy consumption is a multi-variable coupled nonlinear system.…”

Section: Related Workmentioning

confidence: 99%

Comparison and Analysis of Prediction Models for Locomotive Traction Energy Consumption Based on the Machine Learning

et al. 2023

View full text Add to dashboard Cite

Locomotive traction energy consumption is a multivariate coupled nonlinear system closely related to many factors such as locomotive properties, routing, line conditions, and operating methods. In order to accurately calculate and predict locomotive traction energy consumption, three prediction models are constructed based on a large number of measured operation data of the HXD1 locomotive. This research uses two neural network methods of backpropagation and radial basis functions, as well as support vector machines. Among them, the training group uses 600 data; the validation group uses 200 data. The above methods can compare and analyze prediction performance between different neural networks and machine learning algorithm models. The results show that the RBF and BP neural networks can effectively predict locomotive traction energy consumption among the different neural network models. The R 2 of the two neural network model test groups is 0.9926 and 0.9885, the MAPE is 2.91% and 7.28%, and the MSE is 0.02% and 0.08%, respectively. Moreover, we have avoided the influence of the randomness of the neural network algorithm through repeated running. It shows that RBF neural network is better than BP neural network in predicting locomotive traction energy consumption, with more powerful approximation performance and higher accuracy. Among the different machine learning algorithms, the R 2 of the SVM algorithm model test group is 0.9983, the MAPE is 2.01%, and the MSE is 0.02%, which shows the prediction accuracy and overall performance of the SVM algorithm model is better than the neural network model. Finally, we prove the broad generalization of the SVM algorithm through the application on other lines. The SVM algorithm model can be a powerful tool for calculating and predicting the traction energy consumption of HXD1 locomotives.

show abstract

Section: Related Workmentioning

confidence: 99%