📕

Publications

International Journal

•

(2025) Quantitative comparison of explainable artificial intelligence methods for nuclear power plant accident diagnosis models

◦

SG Kim, S Ryu, K Jin, H Kim*

◦

Progress in Nuclear Energy, JIF top 7%

•

(2024) Data-driven clustering analysis for representative EV charging profile in South Korea

◦

K Kim, G Kim, J Yoo, J Heo, J Cho, S Ryu*, J Kim *

◦

Sensors.

•

(2024) Can Untrained Neural Networks Detect Anomalies?

◦

S Ryu, Y Yu, H Seo.

◦

Transactions on Industrial Informatics. IF 12.3 / *JIF top 2.3%

•

(2024) Quantile-Mixer: A Novel Deep Learning Approach for Probabilistic Short-term Load Forecasting

◦

S Ryu, Y Yu.

◦

Transactions on Smart Grid. IF 9.6 / *JIF top 7%

•

(2023) Development of deep autoencoder-based anomaly detection system for HANARO

◦

S Ryu,B Jeon, H Seo, M Lee, JW Shin, Y Yu. 

◦

Nuclear Engineering and Technology. IF 2.817

•

(2022) Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code.

◦

S Ryu, H Kim, S Kim, K Jin, J Cho, J Park

◦

Expert Systems with Applications. IF 8.665 / *JIF top 10%

•

(2022). Quantile Autoencoder With Abnormality Accumulation for Anomaly Detection of Multivariate Sensor Data. 

◦

S Ryu,J Yim, J Seo, Y Yu, H Seo 

◦

IEEE Access. IF 3.476

•

(2022). An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system. 

◦

K Jin, H Kim, S Ryu, S Kim, J Park. 

◦

Reliability Engineering & System Safety. IF 7.247

•

(2020). Denoising autoencoder based missing value imputation for smart meters. 

◦

S Ryu, M Kim, & H Kim. 

◦

IEEE Access.  IF 3.367

•

(2019). Convolutional Autoencoder based Feature Extraction and Clustering for Customer Load Analysis. 

◦

S Ryu, H Choi, H Lee, & H Kim. 

◦

IEEE Transactions on Power Systems. IF 6.074 / *JIF top 10% 

•

(2019). Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles.

◦

Y Choi, S Ryu, K Park, & H Kim 

◦

IEEE Access, 7, 75143-75152. IF 3.476 127 citations based on Google Scholar.

•

(2018). Gaussian Residual Bidding Based Coalition for Two-Settlement Renewable Energy Market.

◦

S Ryu, S Bae, J Lee, & H Kim.

◦

IEEE Access, 6, 43029-43038. IF 4.098

•

(2017). Robust operation of energy storage system with uncertain load profiles. 

◦

J Kim, Y Choi, S Ryu, & H Kim. 

◦

Energies, 10(4), 416.

•

(2017). Deep neural network-based demand side short term load forecasting. 

◦

S Ryu, J Noh, & H Kim. 

◦

Energies, 10(1), 3. 406 citations based on Google Scholar.

•

(2015). Data-driven baseline estimation of residential buildings for demand response. 

◦

S Park, S Ryu, Y Choi, J Kim, & H Kim. 

◦

Energies, 8(9), 10239-10259.

International conference

•

(2022). Quantile Autoencoder for Anomaly Detection. 

◦

H Seo,S Ryu, J Yim, J Seo, &Y Yu, 

◦

In 2022 AAAI Workshop on AI for Design and Manufacturaing. Workshop presentation at top-tier AI conference

•

(2018). Short-term load forecasting based on ResNet and LSTM. 

◦

H Choi, S Ryu, & H Kim. 

◦

In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.

•

(2018). Residential load profile clustering via deep convolutional autoencoder. 

◦

S Ryu, H Choi, H Lee, H Kim, & V. W. Wong

◦

In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.

•

(2017). Coalition-based bidding strategies for integrating renewable energy sources in electricity market. 

◦

S Bae, S Ryu, & H Kim. 

◦

In 2017 IEEE Power & Energy Society General Meeting (pp. 1-5). IEEE.

•

(2014). A framework for baseline load estimation in demand response: Data mining approach. 

◦

S Park, S Ryu, Y Choi, & H Kim. 

◦

In 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 638-643). IEEE.