Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Pulsed Electromagnetic Field (PEMF) Stimulation for the Treatment of Fifth Metatarsal Fracture Nonunion.

Orthopedic reviews·2026

Same author

Simultaneous Orthognathic Surgery and Condylectomy for Active Unilateral Condylar Hyperplasia: Indications and Outcomes of a Single-Stage Protocol.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons·2026

Same author

PD-L1-positive circulating tumor cells associate with tumor malignancy and impaired circulating immunity in patients with gastrointestinal tumors.

Scientific reports·2026

Same author

Influenza B virus suppression of Chromosome Y-linked genes increases pulmonary virus replication and disease severity in male mice.

Research square·2026

Same author

Genomic sequencing of multicystic mesothelioma finds cohesin complex mutations associated with disease recurrence in patients referred for cytoreductive surgery and HIPEC.

British journal of cancer·2026

Same author

N-glycosylation at the receptor binding site drives differences in receptor binding specificity between influenza B virus lineages.

Journal of virology·2025

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Feature Extraction from Building Submetering Networks Using Deep Learning.

Antonio Morán¹, Serafín Alonso¹, Daniel Pérez¹

¹Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial, Informática y Aeroespacial, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain.

Sensors (Basel, Switzerland)

|July 8, 2020

Summary

This summary is machine-generated.

This study uses a deep convolutional autoencoder to analyze energy consumption in large buildings. The method accurately reveals how different areas influence overall energy use, even with unbalanced loads.

Keywords:

autoencoders convolutional neural networks deep learning power consumption submetering networks

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Dec 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Building energy analysis
Artificial intelligence in energy systems
Sustainable building strategies

Background:

Understanding large building energy use is crucial for climate strategies.
Submetering networks offer detailed energy consumption data.
Classical analysis methods can fail with unbalanced loads.

Purpose of the Study:

To propose a deep convolutional autoencoder for reconstructing building energy consumption.
To analyze the behavior of different building areas using learned features.
To identify the influence of each area on total consumption and external factors.

Main Methods:

Deployment of a submetering network in a hospital building.
Application of a deep convolutional autoencoder for consumption reconstruction.
Analysis of latent space information and weight distribution.

Main Results:

Accurate reconstruction of whole building consumption from submeter data.
Identification of correlations between environmental variables and building areas.
Grouping of areas based on their functional performance contribution.
Discernible results for areas with varying consumption ranges.

Conclusions:

The deep convolutional autoencoder effectively analyzes building energy consumption patterns.
The method overcomes limitations of classical techniques in unbalanced load scenarios.
Building areas can be understood and grouped by their energy performance contribution.