Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurat...

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Bibliographic Details
Main Authors: Mohammad Irani Azad, Roozbeh Rajabi, Abouzar Estebsari
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Electronics
Subjects:
nonintrusive load monitoring (NILM)
deep learning
attention mechanism
temporal pooling
residual connections
transformers
Online Access:https://www.mdpi.com/2079-9292/13/2/407

Internet

https://www.mdpi.com/2079-9292/13/2/407

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