一个资源高效的机器学习框架,用于实时非侵入性负载监控和太阳能航空系统的性能优化
Aguida Mohammed Echarif1, Yanbo Che2, El Moundher Aouiche3
1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
Scientific reports
|December 8, 2025
概括
对于太阳能航空,K-Nearest Neighbors (KNN) 在实时提供高效的非侵入性负载监控 (NILM). 传统的机器学习 (ML) 模型在资源有限的飞行系统中优于深度学习 (DL).
科学领域:
- 航空航天工程 航空航天工程
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 非侵入性负载监控 (NILM) 对于太阳能飞行中的智能能源管理至关重要.
- 航空业严格的重量,延迟和计算限制挑战了实时NILM部署.
- 开发资源高效的NILM框架对于可持续的飞行运营至关重要.
研究的目的:
- 评估和比较六个机器学习 (ML) 和深度学习 (DL) 模型的实时NILM在太阳能飞机中的性能.
- 确定最合适的NILM方法,平衡机载航空系统的准确性和计算效率.
- 为下一代太阳能飞机能源管理提供设计见解.
主要方法:
- 利用了高分辨率 (200 kHz) 的功率数据,捕捉了短暂和稳定状态负载特征.
- 应用高级预处理:移动平均平滑和不重叠的下方采样.
- 使用Opal-RT和Launchpad-F28379D DSP控制器进行了硬件在循环中的验证.
主要成果:
- K-最接近的邻居 (KNN) 实现了最佳的准确性-效率平衡 (R2=0.9403,0.20s执行时间).
- 像CNN-LSTM这样的复杂DL模型显示了高精度 (MSE=0.0048),但对于实时使用 (271.53s) 来说太慢了.
- 传统的机器学习模型在资源有限的环境中显示出卓越的实时可行性.
结论:
- 对于航空的模型选择,NILM必须优先考虑计算效率和实时响应能力,而不是原始准确性.
- 优化的传统ML算法比复杂的DL模型更适合在飞机上的太阳能能源管理.
- 这项研究可以在未来的太阳能飞机中实现更加可持续和智能化的能源控制.
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