用轻量模型和动态时间规整，在边缘设备上实现高精度用电设备识别。
A Non-Invasive Load Monitoring Method for Edge Computing Based on MobileNetV3 and Dynamic Time Regulation
- 结合MobileNetV3与动态时间规整，优化时频域特征提取。
- 在边缘MCU上实现95%识别准确率，运行时间减少55.55%。
- 适合资源受限的智能家居能耗监控场景。
近年来，非侵入式负载监测（NILM）技术凭借仅用单个电表数据即可实现设备级能耗分解的独特优势，受到广泛关注。基于机器学习与深度学习的前沿方法通过融合时频域特征，在负载分解精度上取得显著进展。然而，这些方法普遍面临计算开销高、内存占用大的问题，成为其在资源受限的微控制器单元（MCU）上部署的主要障碍。为此，本研究提出一种新型时频域动态时间规整（DTW）算法，并系统比较分析了六种机器学习方法在家用电场景下的性能表现。在边缘MCU上完成完整实验验证后，该方案成功实现95%的识别准确率；同时，对频域特征提取过程进行深度优化，使运行时间降低55.55%，存储开销减少约34.6%。未来研究将进一步优化算法性能。考虑到消除电压互感器设计可显著降低成本，后续工作将聚焦于此方向，致力于为NILM的实际应用提供更经济的解决方案，并为边缘计算环境下高效NILM系统的构建提供坚实的理论基础与可行的技术路径。
原文摘要 · Abstract (English)
In recent years, non-intrusive load monitoring (NILM) technology has attracted much attention in the related research field by virtue of its unique advantage of utilizing single meter data to achieve accurate decomposition of device-level energy consumption. Cutting-edge methods based on machine learning and deep learning have achieved remarkable results in load decomposition accuracy by fusing time-frequency domain features. However, these methods generally suffer from high computational costs and huge memory requirements, which become the main obstacles for their deployment on resource-constrained microcontroller units (MCUs). To address these challenges, this study proposes an innovative Dynamic Time Warping (DTW) algorithm in the time-frequency domain and systematically compares and analyzes the performance of six machine learning techniques in home electricity scenarios. Through complete experimental validation on edge MCUs, this scheme successfully achieves a recognition accuracy of 95%. Meanwhile, this study deeply optimizes the frequency domain feature extraction process, which effectively reduces the running time by 55.55% and the storage overhead by about 34.6%. The algorithm performance will be further optimized in future research work. Considering that the elimination of voltage transformer design can significantly reduce the cost, the subsequent research will focus on this direction, and is committed to providing more cost-effective solutions for the practical application of NILM, and providing a solid theoretical foundation and feasible technical paths for the design of efficient NILM systems in edge computing environments.
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