基于改进的ConvFormer模型预测和分析电动汽车充电站的相对误差
Liwen Chen1, Zhibin Liu1, Qingquan Yu1
1Fujian University of Technology Institute of Ubiquitous Perception and Multi-sensor Integration Research, Fuzhou, Fujian Province, 350118, China.
一个新的ConvFormer模型通过预测计量错误来提高电动汽车充电站的准确性. 与传统模型相比,这种方法显著减少了平均绝对误差和平均平方误差.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电动汽车 (EV) 充电站的计量不准确性阻碍了电动汽车的发展.
- 准确的能源测量对于可靠的电动汽车充电基础设施至关重要.
研究的目的:
- 为电动汽车充电站计量中的相对错误提出一种新的预测方法.
- 提高电动汽车充电站计量数据的准确性.
主要方法:
- 开发了ConvFormer模型,集成卷积神经网络 (CNN) 和变压器模型.
- 使用前置插值和规范化,预处理充电站数据.
- 用于时间序列预测的神经网络的构建,使用平均绝对误差 (MAE) 和平均平方误差 (MSE) 进行评估.
主要成果:
- ConvFormer模型在预测相对错误方面表现出卓越的性能.
- 与变压器模型相比,实现了MAE的47.30%减少和MSE的66.94%减少.
- 与LSTM模型相比,MAE减少了38.06%,MSE减少了62.32%.
结论:
- 拟议的ConvFormer模型显著提高了预测充电站计量错误的准确性.
- 这一进步支持电动汽车充电基础设施的可靠开发和运行.
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