About
Biography
Seunghyoung Ryu received the B.S., M.S. and Ph.D degree in the Electronic Engineering from Sogang University, South Korea in 2014, 2016, and 2020, respectively. He is currently a Senior Research Engineer of the Intelligent Computing Lab, Korea Atomic Energy Research Institute, South Korea. His research interests are transactive energy, energy data analysis, energy forecasting, machine learning and artficial intelligence in smart grid.
한국원자력연구원 인공지능응용전략실 / 선임연구원
주요 연구 분야 : 이상탐지, 시계열 예측, 스마트 그리드, 에너지 데이터 분석, 머신 러닝.
Publications
Journal papers
- Development of deep autoencoder-based anomaly detection system for HANARO
- S Ryu, B Jeon, H Seo, M Lee, J Shin, Y Yu, Nuclear Engineering and Technology, Accepted, 2022
- 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, Aug. 2022
- Quantile Autoencoder with Abnormality Accumulation for Anomaly Detection of Multi-variate Sensor Data
- S Ryu, J Yim, J Seo, Y Yu, H Seo, IEEE Access, Jun. 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, Jun. 2022
- Denoising Autoencoder-Based Missing Value Imputation for Smart Meters
- S. Ryu, M. Kim and H. Kim, IEEE Access, pp.40656 - 40666, Feb. 2020
- Convolutional Autoencoder based Feature Extraction and Clustering for Customer Load Analysis
- S. Ryu, H. Choi, H. Lee and H. Kim, Transactions on Power Systems, Aug. 2019
- Machine Learning based Lithium-Ion Battery Capacity Estimation Exploiting Charging Features
- Y. Choi, S. Ryu, K. Park and H. Kim, IEEE Access, pp.75143 - 75152, Jun. 2019
- Gaussian Residual Bidding based Coalition for Two-settlement Renewable Energy Market
- S. Ryu, S. Bae, J. Lee and H. Kim, IEEE Access, pp.43029 - 43038, Aug. 2018
- Robust Operation of Energy Storage System with Uncertain Load Profiles
- J. K. Kim, Y. Choi, S. Ryu and H. Kim, Energies, pp.1-15, Mar. 2017.
- Deep Neural Network based Demand Side Short-Term Load Forecasting
- S. Ryu, J. Noh, and H. Kim, Energies, pp.1-20, Jan. 2017.
- Customer Load Pattern Analysis using Clustering Techniques
- S. Ryu, D. Oh, J. Noh and H.Kim, KEPCO Journal on Electric Power and Energy, pp.61-69, 2016
- Data-Driven Baseline Estimation of Residential Buildings for Demand Response
- S. Park, S. Ryu, Y. Choi, J. Kim and H. Kim, Energies, pp.10239-10259, 2015.
Selected Conference Proceedings
- 합성곱 신경망 기반 회귀 문제에 대한 클래스 활성화 맵 활용에 관한 연구
- 임소영， 문지유， 류승형, 한국통신학회 동계학술대회, 2022
- 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), 2022
- MLP-Mixer 기반 단기 전력부하 예측 모델에 관한 연구
- 류승형, 한국통신학회 추계학술대회, 2021
- 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
- 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, 2021
- Study on the 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, 2021 Korean Nuclear Society Virtual Autumn Meeting, 2021
- Probabilistic deep learning based fast running model of thermal-hydraulic code
- S Ryu, H Kim, SG Kim, K Jin, J Cho, J Park, 2021 Korean Nuclear Society Virtual Autumn Meeting, 2021
- 트라이볼로지 분야의 인공지능 적용 사례 탐색
- 유용균， *류승형8, 한국트라이볼로지학회 학술대회, 2020
- A Survey on Artificial Intelligence in Nuclear Science
- S Lee, S Ryu, K Lim, Y Yu, 2020 Korean Nuclear Society Virtual Spring Meeting
- 협력적 게임 이론 기반 분산 신재생 발전 자원의 통합 운용 및 전력 시장 참여에 대한 연구
- 류승형, 김홍석, 한국통신학회 동계종합학술발표회 논문집, 2019
- ResNet과 LSTM을 이용한 전력 수요 예측
- 최현근, 류승형, 김홍석, 한국통신학회 동계종합학술발표회 논문집, 2019
- Short-Term Load Forecasting based on ResNet and LSTM
- H. Choi, S. Ryu and H. Kim, IEEE SmartGridComm, 2018, pp.1-6.
- Residential Load Profile Clustering via Deep Convolutional Autoencoder
- S.Ryu, H. Choi, H. Lee, H. Kim, and V. W.S. Wong, IEEE SmartGridComm, 2018, pp 1-6.
- Convolutional Autoencoder 기반 소규모 수용가 부하 프로파일 클러스터링에 대한 연구
- 류승형, 최현근, 이효섭, 김홍석, 한국통신학회 학술대회논문집, 22-24, 2017
- Coalition-based Bidding Strategies for Integrating Renewable Energy Sources in Electricity Market
- S. Bae, S. Ryu and H. Kim, IEEE Power and Energy Society General Meeting (PES-GM), 2017, pp.1-5.
- 시간 지연을 고려한 소규모 수용가 부하 프로파일 클러스터링
- 류승형, 김홍석, JCCI, 2017
- Deep Neural Network Based Demand Side Short Term Load Forecasting
- S. Ryu, J. Noh and H. Kim, IEEE SmartGridComm, 2016, pp.1-6
- 심층신경망 기반 전력수요예측 모델에 대한 연구
- 류승형, 노재구, 김홍석, 한국통신학회 학술대회논문집, 488-489, 2016
- Hierarchical Clustering of Load Profile Database in Understanding of Korean Standard Industrial Classification
- S. Ryu, H. Kim, D. Oh, J.-i. Lee and J. Noh, International Smart Grid Conference (ISGC), Oct. 2015
- A Framework for Baseline Load Estimation in Demand Response: Data Mining Approach
-
S. Park, S. Ryu, Y. Choi and H. Kim, IEEE SmartGridComm, 2014, pp.1-6
-
Invited Talks
- 딥러닝 기반 이상탐지 기술, 재난안전 데이터&AI 워크샵, KISTI부울경지원, 22.07
- 딥러닝을 활용한 이상탐지 기초이해, 2022 AAICON 학술대회, AIFrenz, 22.07
- 이상탐지를 위한 인공지능 기술, 패밀리기업인공지능교육, KAERI, 22.06
- 인공지능을 활용한 데이터 기반 이상탐지 기술, 인공지능 산업사례 교육워크샵, AIFrenz, 22.05
- 원자력 분야의 확률적 딥러닝 모델 활용, 연구세미나, 한국전기연구원, 21.12
- Dynamic PSA 시뮬레이션을 위한 인공지능 기술, 디지털트윈 미니워크샵, UNIST 원자력공학과, 21.12
- AI 및 고속예측모델을 활용한 원자로 이상 탐지, 환경IT 융합세미나, 충남대 환경공학과, 21.10
- PSA 고속시뮬레이션 및 이상탐지, 원자력-인공지능 융합 심포지엄, UNIST 원자력공학과, 21.07
Projects
- Optimal Control of Virtual Power Plant for Smart City (2016.10 ~ 2019.5) funded by KEPCO KEPRI.
- Community Energy Service Microgrid (2016.5 ~ 2018.12) funded by KETEP.
- National Demand Response (2016.5 ~ 2018.12) funded by KETEP.
- Korean Energy Storage System for Cellular Networks (2014.5 ~ 2017.4) funded by NRF.
- Peer to Peer Energy Prosumer Transaction (2016.9~2017.1) funded by ETRI.
- IoT and Optimal Network Design (2014.6~2015.5) funded by LG Electronics.
- ESS Optimal Control and Hardware Development (2014.9~2015.2) funded by NRF and Blue Kite.
- Energy ICT Convergence Platform based on Fast Demand Response (2013.9~2014.8) funded by NIPA.
- D2D System Assisted by Base Stations (2013.6~2014.5) funded by LG Electronics.
Awards
- 2018 / Qualcomm & Sogang University / Qualcomm Best Paper Award / Gaussian Residual Bidding based Coalition for Two-settlement Renewable Energy Market / 최우수
- 2018 / Qualcomm & Sogang University / Qualcomm Best Paper Award / Convolutional Autoencoder based Feature Extraction and Clustering for Customer Load Analysis / 장려
- 2016 / 전력 거래소 / 에너지데이터를 활용한 사업모델 경진 대회 / 에너또 / 우수상
- 2016 / 서강대학교 / 융합 기술 경진 대회 / Cal-log
- 2014 / 서강대학교 전자공학과 / 우수 조교 선정 / 최우수
- 2013 / 서강대학교 전자공학과 / 전자공학과 학술제 캡스톤 디자인 / WPS 기반 모바일 출석 체크 어플리케이션/ 동상