# Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach

arXiv: https://arxiv.org/abs/2105.10302 · HTML: https://mlsanity.com/paper/2105.10302/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 3 of 3)

- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Achieving Differential Privacy against Non-Intrusive Load Monitoring in Smart Grid: a Fog Computing approach](https://mlsanity.com/paper/1804.01817/) (2018)
- [Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors](https://mlsanity.com/paper/1911.13257/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs](https://mlsanity.com/paper/2608.18780/) (2026)

## Abstract (arXiv)

Non-Intrusive 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 MCU-based meters is currently an open challenge. This paper 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's implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design.