相关实验视频
充电站使用基于LSTM的混合变压器模型进行需求预测
Adil Hussain1, Vishwanath Eswarakrishnan2, Ayesha Aslam3
1School of Electronics and Control Engineering, Chang'an University, Xi'an, 710000, China. 2022032907@chd.edu.cn.
Scientific reports
|October 21, 2025
概括
准确的电动汽车 (EV) 充电需求预测对于电网稳定至关重要. 一个新的LSTM-变压器模型显著改善了中长期电动汽车充电需求预测,优于传统方法.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的电动汽车 (EV) 能源需求预测对于电力系统稳定性和充电站可靠运行至关重要.
- 根据历史数据分析充电需求模式,中长期预测至关重要.
研究的目的:
- 提出和评估一种基于LSTM的新型编码解码器变压器模型,用于预测电动汽车充电站 (EVCS) 需求.
- 使用现实数据集,将拟议模型的性能与传统的LSTM和变压器模型进行比较.
主要方法:
- 开发了一个混合LSTM-变压器模型,集成了一个基于LSTM的编码器-解码器架构.
- 在ACN的开放数据集上训练和测试该模型,特别是Caltech和JPL的充电数据.
- 使用平均绝对误差 (MAE) 和平均平方误差 (MSE) 预测未来30,120和240天的需求预测的评估性能.
主要成果:
- 对于加州理工学院和JPL数据集来说,LSTM-Transformer模型显示了与基线模型相比的显著改进.
- 对于加州理工学院的数据,MAE和MSE在30天的时间里分别减少了17.27%和19.79%.
- 对于JPL数据,MAE和MSE的减少在30天后达到24.91%和23.17%,在更长的时间内有持续的改善.
结论:
- 拟议的LSTM-变压器模型有效地提高了中长期电动汽车充电需求预测的准确性.
- 混合模型的性能优于传统的深度学习方法,为电力系统管理和充电基础设施规划提供更可靠的预测.
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