Cargando…
Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on ene...
Autores principales: | Kaselimi, Maria, Protopapadakis, Eftychios, Voulodimos, Athanasios, Doulamis, Nikolaos, Doulamis, Anastasios |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371074/ https://www.ncbi.nlm.nih.gov/pubmed/35957428 http://dx.doi.org/10.3390/s22155872 |
Ejemplares similares
-
A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring
por: Kaselimi, Maria, et al.
Publicado: (2022) -
Deep Learning for Computer Vision: A Brief Review
por: Voulodimos, Athanasios, et al.
Publicado: (2018) -
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
por: Sykiotis, Stavros, et al.
Publicado: (2022) -
Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction
por: Kopsiaftis, George, et al.
Publicado: (2019) -
A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images
por: Voulodimos, Athanasios, et al.
Publicado: (2021)