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A frequency item mining based energy consumption prediction method for electric bus

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  • Zhao, Li
  • Ke, Hanchen
  • Huo, Weiwei

Abstract

For a data-driven bus line energy consumption prediction model, building it with statistical indicators on some variables appeared in the whole bus route, such as average speed, maximum acceleration, etc., always decreases its prediction accuracy due to the discarding of the hidden information in the variable change process. To deal with this problem, a frequency item mining based energy consumption prediction method was proposed, in which the useful prediction information hidden in the process of change is mined by frequency item statistics algorithm and stepwise regression algorithm is used to find the optimal combination of input variables. Simulation and experimental analysis show that with multi-dimensions frequency items, the proposed algorithm can describe and reflect the correlation between different input variables appeared in the process. At the same time, a lot of hardware and software computing costs are saved.

Suggested Citation

  • Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222028018
    DOI: 10.1016/j.energy.2022.125915
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    1. Zhao, Li & Li, Yuqi & Li, Shuai & Ke, Hanchen, 2023. "A frequency item mining based embedded feature selection algorithm and its application in energy consumption prediction of electric bus," Energy, Elsevier, vol. 271(C).

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