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25 October 2023 Forecasting of the charging load for electric vehicle based on a hybrid model
Hao Li, Jianjun Tuo, Yarong Ma, Jing Yang, He Liao, Yuxin Feng
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Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 128011F (2023) https://doi.org/10.1117/12.3007414
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China
Abstract
With the fast progress of electric vehicles, the load of charging stations plays an increasingly important impact on the power grid. To guarantee the safe operation of power grid, a prediction model with the Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) is investigated. Firstly, considering that there exist strong randomness and volatility in the data of electric vehicles charging load, we adopt the VMD algorithm to decompose the data into three modal components, for reducing the complexity of original data. In addition, decomposition error is also been decomposed into two modal components, for extracting the hidden information in the error. Thus, five subsequences are respectively predicted by the LSTM method, and summation the prediction results constitutes the forecasting of the charging load. Furthermore, experimental analysis indicates that the model is more appropriate for predicting the charging load.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hao Li, Jianjun Tuo, Yarong Ma, Jing Yang, He Liao, and Yuxin Feng "Forecasting of the charging load for electric vehicle based on a hybrid model", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 128011F (25 October 2023); https://doi.org/10.1117/12.3007414
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KEYWORDS
Data modeling
Modal decomposition
Algorithm development
Matrices
Power grids
Neural networks
Random forests
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