Neural Fourier Energy Disaggregation
Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models are usually computationally expensive and they ha...
Main Authors: | Christoforos Nalmpantis, Nikolaos Virtsionis Gkalinikis, Dimitris Vrakas |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/22/2/473 |
Similar Items
-
Variational Regression for Multi-Target Energy Disaggregation
by: Nikolaos Virtsionis Gkalinikis, et al.
Published: (2023-02-01) -
Torch-NILM: An Effective Deep Learning Toolkit for Non-Intrusive Load Monitoring in Pytorch
by: Nikolaos Virtsionis Gkalinikis, et al.
Published: (2022-04-01) -
A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
by: Luca Massidda, et al.
Published: (2022-06-01) -
MC-NILM: A Multi-Chain Disaggregation Method for NILM
by: Hao Ma, et al.
Published: (2021-07-01) -
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey
by: Ahmed Zoha, et al.
Published: (2012-12-01)