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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.