Energy Consumption Prediction of Public Buildings Based on MEA-BP Neural Network

Wang, Shuai; Dou, Jiaming; Ye, Yongxue; Yang, Jingjian

doi:10.1109/ccdc52312.2021.9602564

Cited by 2 publications

(1 citation statement)

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“…In response to the complex and nonlinear characteristics of locomotive traction energy consumption, scholars at home and abroad have proposed various calculation and prediction methods. Wang et al [15] used BP neural network theory to establish a model for calculating locomotives' traction energy consumption and used the draft gauge calculation results for verification. Liang et al [16] proposed an energy consumption prediction model for freight locomotives based on the particle swarm optimization neural network algorithm.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Related Workmentioning

confidence: 99%