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Seunghyoung Ryu
Title
Cited by
Cited by
Year
Deep neural network based demand side short term load forecasting
S Ryu, J Noh, H Kim
Energies 10 (1), 3, 2016
4732016
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
1842019
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
1052019
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
1002018
Denoising autoencoder-based missing value imputation for smart meters
S Ryu, M Kim, H Kim
IEEE Access 8, 40656-40666, 2020
762020
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
652015
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
462014
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
252018
Robust operation of energy storage system with uncertain load profiles
J Kim, Y Choi, S Ryu, H Kim
Energies 10 (4), 416, 2017
252017
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
142022
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
142018
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
132016
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
72023
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
72022
심층신경망 기반 전력수요예측 모델에 대한 연구
유승형, 노재구, 김홍석
한국통신학회 학술대회논문집, 488-489, 2016
72016
Quantile-Mixer: A Novel Deep Learning Approach for Probabilistic Short-term Load Forecasting
S Ryu, Y Yu
IEEE Transactions on Smart Grid, 2023
42023
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
32022
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
32021
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
22021
Evaluation of deep autoencoder based anomaly detection with cold neutron source facility in HANARO
S Ryu, B Jeon, M Lee, Y Yu
2021 Korean Nuclear Society Virtual Autumn Meeting 300 (69), 89, 2021
22021
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