International Journal
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(2024) Can Untrained Neural Networks Detect Anomalies?
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S Ryu, Y Yu, H Seo.
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Transactions on Industrial Informatics. To be published *JIF top 2.3%
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(2024) Quantile-Mixer: A Novel Deep Learning Approach for Probabilistic Short-term Load Forecasting
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S Ryu, Y Yu.
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Transactions on Smart Grid. IF 9.6 / *JIF top 7%
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(2023) Development of deep autoencoder-based anomaly detection system for HANARO
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S Ryu,B Jeon, H Seo, M Lee, JW Shin, Y Yu.
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Nuclear Engineering and Technology. IF 2.817
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(2022) Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code.
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S Ryu, H Kim, S Kim, K Jin, J Cho, J Park
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Expert Systems with Applications. IF 8.665 / *JIF top 10%
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(2022). Quantile Autoencoder With Abnormality Accumulation for Anomaly Detection of Multivariate Sensor Data.
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S Ryu,J Yim, J Seo, Y Yu, H Seo
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IEEE Access. IF 3.476
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(2022). An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system.
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K Jin, H Kim, S Ryu, S Kim, J Park.
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Reliability Engineering & System Safety. IF 7.247
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(2020). Denoising autoencoder based missing value imputation for smart meters.
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S Ryu, M Kim, & H Kim.
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IEEE Access. IF 3.367
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(2019). Convolutional Autoencoder based Feature Extraction and Clustering for Customer Load Analysis.
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S Ryu, H Choi, H Lee, & H Kim.
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IEEE Transactions on Power Systems. IF 6.074 / *JIF top 10%
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(2019). Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles.
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Y Choi, S Ryu, K Park, & H Kim
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IEEE Access, 7, 75143-75152. IF 3.476 127 citations based on Google Scholar.
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(2018). Gaussian Residual Bidding Based Coalition for Two-Settlement Renewable Energy Market.
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S Ryu, S Bae, J Lee, & H Kim.
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IEEE Access, 6, 43029-43038. IF 4.098
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(2017). Robust operation of energy storage system with uncertain load profiles.
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J Kim, Y Choi, S Ryu, & H Kim.
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Energies, 10(4), 416.
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(2017). Deep neural network-based demand side short term load forecasting.
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S Ryu, J Noh, & H Kim.
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Energies, 10(1), 3. 406 citations based on Google Scholar.
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(2015). Data-driven baseline estimation of residential buildings for demand response.
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S Park, S Ryu, Y Choi, J Kim, & H Kim.
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Energies, 8(9), 10239-10259.
International conference
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(2022). Quantile Autoencoder for Anomaly Detection.
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H Seo,S Ryu, J Yim, J Seo, &Y Yu,
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In 2022 AAAI Workshop on AI for Design and Manufacturaing. Workshop presentation at top-tier AI conference
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(2018). Short-term load forecasting based on ResNet and LSTM.
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H Choi, S Ryu, & H Kim.
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In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.
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(2018). Residential load profile clustering via deep convolutional autoencoder.
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S Ryu, H Choi, H Lee, H Kim, & V. W. Wong
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In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.
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(2017). Coalition-based bidding strategies for integrating renewable energy sources in electricity market.
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S Bae, S Ryu, & H Kim.
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In 2017 IEEE Power & Energy Society General Meeting (pp. 1-5). IEEE.
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(2014). A framework for baseline load estimation in demand response: Data mining approach.
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S Park, S Ryu, Y Choi, & H Kim.
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In 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 638-643). IEEE.