相关实验视频
电动汽车充电站的短期需求预测使用上下文感知时间变压器模型
Adil Hussain1, Qing-Chang Lu2, Sanam Shahla Rizvi3
1Department of Traffic Information and Control Engineering, School of Electronics and Control Engineering, Chang'an University, Xi'an, 710064, Shaanxi, China.
Scientific reports
|October 21, 2025
概括
预测电动汽车 (EV) 的充电需求对于电网稳定性至关重要. 一个新的上下文感知时间变压器 (CAT-Former) 模型有效地利用特定位置的时间和上下文数据预测短期电动汽车充电需求.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 越来越多的电动汽车 (EV) 应用给电网带来了压力,需要准确的需求预测.
- 现有的研究往往忽略了特定位置的充电行为模式.
- 了解不同城市的不同充电模式对于电网管理至关重要.
研究的目的:
- 开发和评估一种新的背景感知时间变压器 (CAT-Former) 模型,用于短期预测电动汽车充电需求.
- 结合时间和上下文特征来捕捉特定位置的充电行为.
- 评估模型的性能与已建立的基线和基于变压器的模型相比.
主要方法:
- 利用了科罗拉多州博尔德市三个高流量地点的公共电动汽车充电数据.
- 开发了CAT-Former模型,整合了时间和上下文特征,以提高预测.
- 使用MSE和MAE指标,比较了CAT-Former与LSTM,BiLSTM,CNN-LSTM,CNN-BiLSTM和变压器模型的性能.
主要成果:
- 在预测电动汽车充电需求方面,CAT-Former模型显著超过基线和其他变压器模型.
- 实现了最低的平均平方误差 (MSE) 和平均绝对误差 (MAE) 一小时和一天前的预测.
- 在各种充电环境中证明了时间和上下文特征的有效性.
结论:
- CAT-Former模型为短期电动汽车充电需求预测提供了强大的解决方案.
- 使用特定位置数据进行准确的预测可以减轻电动汽车充电造成的电力供应挑战.
- 该研究强调了情境意识模型对智能电网管理的重要性.
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