通过可解释的联合学习来增强智能城市的可持续性,以控制车辆的能源
Khalid Ishaq Abdullah Almaazmi1, Saif Jasim Almheiri1, Muhammad Adnan Khan2
1School of Computing, Skyline University College, Sharjah, United Arab Emirates.
Scientific reports
|July 4, 2025
概括
本研究介绍了可解释的联合学习 (XFL),以优化智能城市的电动和自动驾驶汽车能源管理. XFL增强了隐私和准确性,同时为高效的电网使用提供了透明的AI决策.
科学领域:
- 人工智能的人工智能
- 智慧城市技术 智慧城市技术
- 车辆能源管理 车辆能源管理
背景情况:
- 智能城市中的电动和自动驾驶汽车面临能源管理挑战,原因是消耗量未经优化和电网使用效率低下.
- 传统的集中式系统在实时数据,隐私和适应性方面扎,导致成本和能源浪费.
- 联邦学习 (FL) 提供分散的培训,但缺乏透明度,妨碍对AI决策的信任.
研究的目的:
- 引入一个可解释的联合学习 (XFL) 框架,以优化智能城市中的车辆能源管理.
- 提高隐私,减少通信开销,提高能源管理系统的实时适应性.
- 将可解释性技术集成到FL中,以实现透明和负责任的AI驱动决策.
主要方法:
- 开发了一个层次化的FL架构,用于使用大型AEV遥测数据集进行安全,分散的学习.
- 集成可解释AI (XAI) 技术与FL一起创建拟议的XFL框架.
- 利用基于多层感知器 (MLP) 的全球模型来分析流量,能量状态和电网负载.
主要成果:
- 实现了卓越的预测准确性,能源消耗的R2值为94.73%,交通密度为99.83%.
- 证明了对实时交通,车辆能量状态和电网负载平衡的有效分析,同时保持数据隐私.
- 在车辆能源管理优化方面显著优于以前的方法.
结论:
- 拟议的XFL框架通过将分散的学习与可解释性相结合,有效地优化智能城市的车辆能源管理.
- XFL提高了对人工智能驱动的能源管理系统的信任和监管合规性.
- 这种方法为智能城市移动的日益复杂性提供了一个可扩展和保护隐私的解决方案.
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