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

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-...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on industrial informatics Vol. 18; no. 2; pp. 943 - 952
Main Authors:	Tabanelli, Enrico, Brunelli, Davide, Acquaviva, Andrea, Benini, Luca
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Cost reduction Edge processing Electric potential Electricity meters Feature extraction Hardware Home appliances low-power microcontroller units (MCUs) Machine learning machine learning (ML) Meters Microcontrollers nonintrusive load monitoring (NILM) Optimization Performance evaluation Power consumption Real-time systems Sensors Voltage Voltage measurement
ISSN:	1551-3203, 1941-0050
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!