| Deep neural network based demand side short term load forecasting S Ryu, J Noh, H Kim Energies 10 (1), 3, 2016 | 523 | 2016 |
| 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, 2019 | 238 | 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 35 (2), 1048-1060, 2019 | 142 | 2019 |
| Short-term load forecasting based on ResNet and LSTM H Choi, S Ryu, H Kim 2018 IEEE International Conference on Communications, Control, and Computing …, 2018 | 118 | 2018 |
| Denoising autoencoder-based missing value imputation for smart meters S Ryu, M Kim, H Kim IEEE Access 8, 40656-40666, 2020 | 96 | 2020 |
| 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, 2015 | 71 | 2015 |
| A framework for baseline load estimation in demand response: Data mining approach S Park, S Ryu, Y Choi, H Kim 2014 IEEE International Conference on Smart Grid Communications …, 2014 | 48 | 2014 |
| Residential load profile clustering via deep convolutional autoencoder S Ryu, H Choi, H Lee, H Kim, VWS Wong 2018 IEEE international conference on communications, control, and computing …, 2018 | 31 | 2018 |
| Robust operation of energy storage system with uncertain load profiles J Kim, Y Choi, S Ryu, H Kim Energies 10 (4), 416, 2017 | 29 | 2017 |
| Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code S Ryu, H Kim, SG Kim, K Jin, J Cho, J Park Expert Systems with Applications 200, 116966, 2022 | 27 | 2022 |
| 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 55 (2), 475-483, 2023 | 18 | 2023 |
| Gaussian residual bidding based coalition for two-settlement renewable energy market S Ryu, S Bae, JU Lee, H Kim IEEE Access 6, 43029-43038, 2018 | 14 | 2018 |
| Quantile-mixer: A novel deep learning approach for probabilistic short-term load forecasting S Ryu, Y Yu IEEE Transactions on Smart Grid, 2023 | 13 | 2023 |
| Customer load pattern analysis using clustering techniques S Ryu, H Kim, D Oh, J No KEPCO Journal on Electric Power and Energy 2 (1), 61-69, 2016 | 13 | 2016 |
| Quantile autoencoder with abnormality accumulation for anomaly detection of multivariate sensor data S Ryu, J Yim, J Seo, Y Yu, H Seo IEEE Access 10, 70428-70439, 2022 | 11 | 2022 |
| 심층신경망 기반 전력수요예측 모델에 대한 연구 유승형， 노재구， 김홍석 한국통신학회 학술대회논문집, 488-489, 2016 | 7 | 2016 |
| 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 222, 108446, 2022 | 6 | 2022 |
| Enhancing the Explainability of AI Models in Nuclear Power Plants with Layer-wise Relevance Propagation SG Kim, S Ryu, H Kim, K Jin, J Cho 2021 Korean Nuclear Society Virtual Autumn Meeting, 2021 | 4 | 2021 |
| Quantile autoencoder for anomaly detection H Seo, S Ryu, J Yim, J Seo, Y Yu AAAI 2022 Workshop on AI for Design and Manufacturing (ADAM), 2021 | 3 | 2021 |
| Can Untrained Neural Networks Detect Anomalies? S Ryu, Y Yu, H Seo IEEE Transactions on Industrial Informatics, 2024 | 2 | 2024 |
Hongseok KimProfessor of Electronic Engineering, Sogang UniversityAdresse e-mail validée de sogang.ac.kr