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. Jedoch, 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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