Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of in...

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Bibliographic Details
Main Authors: Anthony Faustine, Lucas Pereira
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Energies
Subjects:
multi-label learning
Non-intrusive Load Monitoring
appliance recognition
fryze power theory
V-I trajectory
Convolutional Neural Network
Online Access:https://www.mdpi.com/1996-1073/13/16/4154

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https://www.mdpi.com/1996-1073/13/16/4154

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