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

TL;DR

This paper presents a systematic approach to optimize feature extraction and inference for resource-constrained MCU-based NILM, achieving high accuracy with reduced computational and storage costs, enabling practical edge deployment.

Contribution

It introduces a method to trim feature spaces and optimize NILM algorithms for low-cost MCUs, maintaining high accuracy while significantly reducing resource usage.

Findings

Achieved 95.15% accuracy with optimized features on MCU

Up to 5.45x speed-up and 80.56% storage reduction

Near 80% accuracy using only current measurements

Abstract

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.