Skip to main navigation
Skip to search
Skip to main content
Korea University Pure Home
Home
Profiles
Research units
Equipment
Research output
Press/Media
Search by expertise, name or affiliation
Improving electric energy consumption prediction using CNN and Bi-LSTM
Tuong Le
, Minh Thanh Vo
, Bay Vo
,
Eenjun Hwang
, Seungmin Rho
, Sung Wook Baik
*
*
Corresponding author for this work
Research output
:
Contribution to journal
›
Article
›
peer-review
246
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Improving electric energy consumption prediction using CNN and Bi-LSTM'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Convolutional Neural Network
100%
Bidirectional Long Short-term Memory (BiLSTM)
100%
Electricity Consumption Forecasting
100%
Electric Energy Consumption
71%
Individual Houses
42%
Two-directional
14%
Several Variables
14%
Performance Prediction
14%
Performance Metrics
14%
Power Management System
14%
State-of-the-art Models
14%
Fully Connected Layer
14%
Energy Consumption Prediction
14%
Energy Development Policy
14%
Intelligent Power Management
14%
Engineering
Directional
100%
Electric Energy Consumption
100%
Long Short-Term Memory
100%
Convolutional Neural Network
100%
Electric Power Utilization
33%
Experimental Result
11%
Metrics
11%
Prediction Performance
11%
Drawing-In
11%
Power Management System
11%
Energy Development
11%
Computer Science
Energy Consumption
100%
Convolutional Neural Network
100%
Bidirectional Long Short-Term Memory Network
100%
Power Consumption
33%
Experimental Result
11%
Prediction Model
11%
Complex Task
11%
Performance Metric
11%
Prediction Performance
11%
Power Management System
11%
Fully Connected Layer
11%
Mathematics
Convolutional Neural Network
100%
Several Variables
33%
Connected Layer
33%
Medium Term
33%
Earth and Planetary Sciences
Bidirectional Long Short-Term Memory
100%