Cargando…
Feature Extraction from Building Submetering Networks Using Deep Learning
The understanding of the nature and structure of energy use in large buildings is vital for defining novel energy and climate change strategies. The advances on metering technology and low-cost devices make it possible to form a submetering network, which measures the main supply and other intermedi...
Autores principales: | Morán, Antonio, Alonso, Serafín, Pérez, Daniel, Prada, Miguel A., Fuertes, Juan José, Domínguez, Manuel |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374320/ https://www.ncbi.nlm.nih.gov/pubmed/32629956 http://dx.doi.org/10.3390/s20133665 |
Ejemplares similares
-
A Deep Learning Approach for Fusing Sensor Data from Screw Compressors
por: Alonso, Serafín, et al.
Publicado: (2019) -
A large dataset of detection and submeter-accurate 3-D trajectories of juvenile Chinook salmon
por: Martinez, Jayson, et al.
Publicado: (2021) -
Maximum entropy methods for extracting the learned features of deep neural networks
por: Finnegan, Alex, et al.
Publicado: (2017) -
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
por: Perez, Husein, et al.
Publicado: (2019) -
Learning Sets of Bayesian Networks
por: Cano, Andrés, et al.
Publicado: (2020)