Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection

The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows...

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Bibliographic Details
Main Authors: Inoussa Laouali, Antonio Ruano, Maria da Graça Ruano, Saad Dosse Bennani, Hakim El Fadili
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Energies
Subjects:
non-intrusive load monitoring
energy disaggregation
low frequency power data
convex hull
bidirectional long short time memory
convolutional neural networks
Online Access:https://www.mdpi.com/1996-1073/15/3/1215

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https://www.mdpi.com/1996-1073/15/3/1215

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