Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach
This work addresses the problem of enabling efficient NILM on low-cost edge devices for energy monitoring applications, representing an incremental optimization of existing methods.
The paper tackles the challenge of running low-latency Non-Intrusive Load Monitoring (NILM) on resource-constrained MCU-based meters by optimizing feature spaces and reducing computational and storage costs, achieving 95.15% accuracy with a 5.45x speed-up and 80.56% storage reduction compared to more accurate but demanding methods.
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.