CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption

CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of differe...

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Bibliographic Details
Main Authors: Olamide Jogunola, Bamidele Adebisi, Khoa Van Hoang, Yakubu Tsado, Segun I. Popoola, Mohammad Hammoudeh, Raheel Nawaz
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Energies
Subjects:
hybrid deep learning
convolutional neural network
bidirectional long short-term memory
energy consumption prediction
autoencoder
Online Access:https://www.mdpi.com/1996-1073/15/3/810

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https://www.mdpi.com/1996-1073/15/3/810

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