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Nonintrusive load monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-art approaches are based on machine learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost resource-constrained microcontroller unit (MCU)-based meters is currently an open challenge. This article addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing state-of-the-art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline&#x0027;s implementation on an MCU-based <italic>Smart Measurement Node</italic>. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15&#x0025; accuracy, resulting in a small drop compared to the most accurate feature vector deployment (96.19&#x0025;) while achieving up to 5.45&#x00D7; speedup and 80.56&#x0025; storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80&#x0025; accuracy, allowing a major cost reduction by removing voltage sensors from the hardware (HW) design.

Trimming Feature Extraction and Inference for MCU-Based Edge NILM: A Systematic Approach