Volume 24 Issue 5
Oct.  2024
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(5): 40-53
ZHANG Yu-fei, JIANG Wei, ZHANG Shuo, WANG Teng, XIAO Jing-jing, YUAN Dong-dong. Energy load forecasting of highway facilities in response to integration transportation and energy needs[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 40-53. doi: 10.19818/j.cnki.1671-1637.2024.05.004
Citation: ZHANG Yu-fei, JIANG Wei, ZHANG Shuo, WANG Teng, XIAO Jing-jing, YUAN Dong-dong. Energy load forecasting of highway facilities in response to integration transportation and energy needs[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 40-53. doi: 10.19818/j.cnki.1671-1637.2024.05.004
shu

Energy load forecasting of highway facilities in response to integration transportation and energy needs

doi: 10.19818/j.cnki.1671-1637.2024.05.004
  • 1.

    Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    School of Civil Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2021YFB1600200

National Natural Science Foundation of China 52122809

Key Research and Development Program of Shaanxi Province 2024SF-YBXM-577

Shaanxi Province Qin Chuangyuan "Scientist+Engineer" Team Construction Project 2024QCY-KXJ-020

More Information
  • Author Bio:

    ZHANG Yu-fei(1992-), male, doctoral student, zyufei@chd.edu.cn

    JIANG Wei(1983-), male, professor, PhD, jiangwei@chd.edu.cn

  • Received Date: 2024-04-15
    Available Online: 2024-12-20
  • Publish Date: 2024-10-25
    Abstract
  • To accurately predict the energy load, historical energy load data of typical highway structures were investigated, and the influences of multiple factors on energy load, including traffic volume, weather condition, week, month, and holiday/workday, were analyzed. The principal component analysis (PCA) method was applied to reduce the dimensionality of these influencing factors, thereby eliminating the redundancy in original sequences. The effects of holiday/workday attributes and weather condition on energy load characteristics were examined, and a strategy for constructing a candidate dataset (CD) for energy load forecasting models of highways was proposed. On this basis, the dynamic time modeling for multivariate energy load forecasting was performed by using a long short-term memory (LSTM) network model. The proposed forecasting model was validated by using empirical data from the Guilin-Liuzhou Highway in Guangxi. Analysis results show that the cumulative contribution rate of the five principal components of traffic volume, weather condition, week, month, and holiday/workday, is 85.54%, and they are the primary influencing factors of energy load on highways. The peak energy load periods for tunnels, toll stations, and service areas vary, with tunnel energy load peaks showing minimal fluctuations, toll station peaks concentrated between 10:00-21:00, and service area peaks occurring within the shortest period, only concentrated between 11:00-12:00. Across different seasonal test days, the application of CD construction strategy and PCA processing improves the specificity of the training data, resulting in enhanced prediction accuracy, with the mean absolute percentage error not exceeding 12.33% and the root mean square error not exceeding 3.86. The proposed prediction model demonstrates strong applicability for load prediction in various typical scenarios and provides theoretical support for the design of self-consistent energy systems for highways.

     

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