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Short-Term Load Forecasting in Smart Grids Using Hybrid Deep Learning
Mashael M. Asiri
, Ghadah Aldehim
, Faiz Abdullah Alotaibi
, Mrim M. Alnfiai
,
Mohammed Assiri
, Ahmed Mahmud
Computer Sciences
King Khalid University
Princess Nourah Bint Abdulrahman University
King Saud University
Taif University
Future University in Egypt
Research output
:
Contribution to journal
›
Article
›
peer-review
46
Scopus citations
Overview
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Dive into the research topics of 'Short-Term Load Forecasting in Smart Grids Using Hybrid Deep Learning'. Together they form a unique fingerprint.
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Engineering
Autoencoder
20%
Convolutional Neural Network
10%
Deep Learning Method
100%
Deep Neural Network
10%
Electricity Consumption
10%
Energy Management System
10%
Energy Usage
10%
Error Rate
10%
Illustrates
10%
Learning Approach
10%
Learning System
10%
Load Forecasting
100%
Long Short-Term Memory
30%
Optimization Approach
10%
Optimization Method
10%
Optimization Technique
30%
Renewable Energy Source
10%
Smart Grid
100%
Neuroscience
Behavior (Neuroscience)
33%
Neural Network
66%
Short-Term Memory
100%
Chemical Engineering
Deep Learning Method
100%
Deep Neural Network
11%
Learning System
11%
Long Short-Term Memory
33%
Neural Network
11%
Mathematics
Convolutional Neural Network
11%
Data Analytics
11%
Deep Learning Method
100%
Deep Neural Network
11%
Error Rate
11%
time interval τ
11%