M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments

We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it...

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Published in:Energies (Basel) 2021-08, Vol.14 (16), p.4765
Main Authors: de-Paz-Centeno, Iván, García-Ordás, María Teresa, García-Olalla, Oscar, Arenas, Javier, Alaiz-Moretón, Héctor
Format: Article
Language:English
Subjects:
convolutional neural network
data interpolation
deep-learning
Energy consumption
Internet of Things
Neural networks
Reconstruction
super resolution of energy
super resolution perception
System effectiveness
Time series
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cited_by cdi_FETCH-LOGICAL-c402t-2cf27713a64f97497fa8bb22fb04b70fd80eadabc507cd7b5093175711a2dda03
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container_issue 16
container_start_page 4765
container_title Energies (Basel)
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creator de-Paz-Centeno, Iván
García-Ordás, María Teresa
García-Olalla, Oscar
Arenas, Javier
Alaiz-Moretón, Héctor
description We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it suitable to effectively replace energy apportioning systems. Experiments demonstrate that M-SRPCNN can effectively reconstruct load curves from single month overall values, outperforming traditional apportioning systems.
doi_str_mv 10.3390/en14164765
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subjects convolutional neural network
data interpolation
deep-learning
Energy consumption
Internet of Things
Neural networks
Reconstruction
super resolution of energy
super resolution perception
System effectiveness
Time series
title M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
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