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NICE LAB
(Networking for Intelligence Computing and Energy LAB)
Machine Learning and Optimization for Energy, Power Systems and 6G Wireless
Directed by Prof. Hongseok Kim
서강대학교 나이스랩
What starts at NICE LAB changes the world.
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NICE LAB에서는 적극적이고 열성적인 석/박사과정 대학원생 및 박사후과정을 모집하고 있습니다. 관심있는 학생들은 홈페이지를 참고하세요.
News
News
✓ Oct. 2023. Big Congratulations! The paper entitled FedAND: Federated Learning Exploiting
Consensus ADMM by Nulling Drift has been accepted at IEEE Transactions on Industrial Informatics (JCR Top 2.3% SCI Journal).
✓ Sep. 2023. Dr. Seunghyung Ryu, a former member of NICE LAB, has joined the faculty of the Department of AI Robot at Sejong University. Congratulations, Prof. Ryu!
✓ June 2023. Big Congratulations! The paper entitled Stochastic Home Energy Scheduling Using Deep Learning-based Scenario Generation and Reduction has been accepted at Applied Energy (JCR Top 6% SCI Journal).
✓ Mar. 2023. The paper entitled Autoencoder-based Recommender System Exploiting Natural Noise Removal has been accepted at IEEE Access.
✓ Mar. 2023. Big Congratulations! The paper entitled Deep Reinforcement Learning based Real-time Renewable Energy Bidding with Battery Control has been accepted at IEEE Transactions on Energy Markets, Policy and Regulation.
✓ Mar. 2023. Dr. Jangkyum Kim, a former member of NICE LAB, has joined the faculty of the Department of Data Science at Sejong University. Congratulations, Prof. Kim!
✓ Oct. 2022. Big Congratulations! NeurIPS 2022, the Top AI conference in the world, accepted our paper entitled Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients.
✓ Sep. 2022. The paper entitled Projection-aware Deep Neural Network for DC Optimal Power Flow Without Constraint Violations has been accepted at IEEE SmartGridComm 2022.
Recent Work with Deep Learning/Energy AI
Recent Work with Deep Learning/Energy AI
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients, K. Kim, J. Oh, J. R. Gardner, A. B. Dieng, and H. Kim, NeurIPS, Nov. 2022
Projection-aware Deep Neural Network for DC Optimal Power Flow Without Constraint Violations, M. Kim and H. Kim, IEEE SmartGridComm, pp.1-6, Oct. 2022
Carbon-Neutral Cellular Network Operation Based on Deep Reinforcement Learning, H. Kim, J. So and H. Kim, Energies, Vol. 15, No. 12, pp.1-13, Jun. 2022.
DeepComp: Deep Reinforcement Learning based Renewable Energy Error Compensable Forecasting, J. Jeong and H. Kim, Applied Energy, Vol. 294, No. 116970, pp.1-12, Jul 2021.
Missing-Insensitive Short-term Load Forecasting Leveraging Autoencoder and LSTM, K. Park, J. Jeong, D. Kim and H. Kim, IEEE Access, Vol. 8, No. 1, pp.206039 - 206048, Nov 2020.
Deep Reinforcement Learning based Renewable Energy Error Compensable Forecasting, J. Jeong and H. Kim, ICLR 2020 Workshop: Tackling Climate Change with Machine Learning, April 2020, pp.1-5.
Missing-insensitive Short-term Load Forecasting Leveraging Autoencoder and LSTM, K. Park, J. Jeong and H. Kim, ICLR 2020 Workshop: Tackling Climate Change with Machine Learning, April 2020, pp.1-5.
Denoising Autoencoder based Missing Value Imputation for Smart Meters, S. Ryu, M. Kim, and H. Kim, IEEE Access, Vol. 8, No. 1, pp.40656 - 40666, Mar. 2020.
Convolutional Autoencoder based Feature Extraction and Clustering for Customer Load Analysis, S. Ryu, H. Choi, H. Lee and H. Kim, IEEE Transactions on Power Systems (TPS), Vol. 35, No. 3, pp.1048-1060, Mar. 2020.
LSTM-based Battery Remaining Useful Life Prediction with Multi-Channel Charging Profiles, K. Park, Y. Choi, W. J. Choi, H.-Y. Ryu and H. Kim, IEEE Access, Vol. 8, No. 12, pp.20786-20798, Jan. 2020.
Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network, J. Jeong and H. Kim, Energies, Vol. 12, No. 23, pp.1-14, Nov. 2019.
Machine Learning based Lithium-ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles, Y. Choi, S. Ryu, K. Park and H. Kim, IEEE Access, Vol. 7, pp.75143-75152, 2019.
Practical Operation Strategies for Energy Storage System under Uncertainty, M. Kim, K. Kim, H. Choi, S. Lee and H. Kim, Energies, Vol. 12, No. 6, pp.1-14, Mar. 2019
Residential Load Profile Clustering via Deep Convolutional Autoencoder, S. Ryu, H. Choi, H. Lee, H. Kim and V. W.S. Wong, IEEE SmartGridComm, October 2018.
Short-Term Load Forecasting based on ResNet and LSTM, H. Choi, S. Ryu and H. Kim, IEEE SmartGridComm, October 2018.
Deep Neural Network based Demand Side Short-Term Load Forecasting, S. Ryu, J. Noh and H. Kim, Energies Vol. 10, No. 1, pp.1-20, Jan. 2017 .
Deep Neural Network based Demand Side Short Term Load Forecasting, S. Ryu, J. Noh and H. Kim, IEEE SmartGridComm, July 2016.
Notices for future members
Notices for future members
✓ I am recruiting a couple of M.S. and Ph.D. students and also Post Doctors who are highly motivated and passionate about extending our knowledge boundary by conducting innovative research, in the area of Energy ICT, Smart Grid Communications. If you are interested in joining NICE LAB, please contact me. (email: hongseok AT sogang dot ac dot kr)
✓ For international applicants: Please customize your research statement along with the research directions of NICE LAB, especially in the area of Machine Learning and Optimization with applications of Power System, Network Economics, Microgrid, Energy Storage System, etc. I’ll not respond to email broadcasting.
News (old)
News (old)
✓ Apr. 2021. The paper entitled "DeepComp: Deep Reinforcement Learning based Renewable Energy Error Compensable Forecasting" has been accepted for Applied Energy.
✓ Jan. 2021. The paper entitled “Cloud Energy Storage System Operation with Capacity P2P Transaction” has been accepted for Energies.
✓ Nov. 2020. The paper entitled "Missing-insensitive Short-term Load Forecasting Leveraging Autoencoder and LSTM" has been accepted for IEEE Access.
✓ Feb. 2020. The paper entitled "Missing-insensitive Short-term Load Forecasting Leveraging Autoencoder and LSTM" has been accepted for ICLR 2020 Workshop: Tackling Climate Change with Machine Learning.
✓ Feb. 2020. The paper entitled "Deep Reinforcement Learning based Renewable Energy Error Compensable Forecasting" has been accepted for ICLR 2020 Workshop: Tackling Climate Change with Machine Learning.
✓ Feb. 2020. The paper entitled "Denoising Autoencoder based Missing Value Imputation for Smart Meters" has been accepted for IEEE Access.
✓ Jan. 2020. The paper entitled "LSTM-based Battery Remaining Useful Life Prediction with Multi-Channel Charging Profiles" has been accepted for IEEE Access.
✓ Nov. 2019. The paper entitled “Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network” has been accepted for Energies.
✓ Aug. 2019. The paper entitled "Convolutional Autoencoder based Feature Extraction and Clustering for Customer Load Analysis" has been accepted for IEEE Transactions on Power Systems (TPS).
✓ June 2019. The paper entitled "Direct Energy Trading of Microgrids in Distribution Energy Market" has been accepted for IEEE Transactions on Power Systems (TPS).
✓ May 2019. The paper entitled "Machine Learning based Lithium-Ion Battery Capacity Estimation Exploiting Charging Features" has been accepted for IEEE Access.
✓ May 2019. NICE Lab alumni, Dr. Sunghwan Bae joins Qualcomm, San Diego, USA. Congratulations!
✓ Apr. 2019. The paper entitled “Two Time-Scale Edge Caching and BS Association for Power-Delay Tradeoff in Multi-Cell Networks” has been accepted for IEEE Transactions on Communications (TCOM).
✓ Mar. 2019. The paper entitled “Practical Operational Strategies for Energy Storage System under Uncertainty” has been accepted for Energies.
✓ July 2018. The paper entitled “Short-Term Load Forecasting based on ResNet and LSTM” has been accepted for IEEE SmartGridComm 2018.
✓ July 2018. The paper “Residential Load Profile Clustering via Deep Convolutional Autoencoder” has been accepted for IEEE SmartGridComm 2018.
✓ July 2018. The paper entitled “Gaussian Residual Bidding-based Coalitional Framework for Renewable Energy Market” has been accepted for IEEE Access.
✓ Oct. 2017. The paper entitled “Towards Harmonious Coexistence in Unlicensed Spectrum: Rational Cooperation of Operators” has been accepted for Sensors.
✓ Sep. 2017. The paper entitled “On Optimal Cooperative Sensing Using Energy Detection in Cognitive Radio” has been accepted for Sensors.
✓ Jul. 2017. Prof. Hongseok Kim is on a sabbatical leave for one year and joined the University of British Columbia as a Visiting Associate Professor hosted by Prof. Vincent Wong (https://www.ece.ubc.ca/~vincentw/Homepage/Home.html).
✓ Jun. 2017. Prof. Hongseok Kim gave a talk “Smart Battery Management System for Future Smart Grid” at LG Chemical R&D Campus, Daejeon.
✓ Jun. 2017. Prof. Hongseok Kim gave a talk “Deep Neural Network for Short-term Load Forecasting” at Chung-Ang University hosted by Prof. Daehyun Choi.
✓ Jun. 2017. Prof. Hongseok Kim gave a talk “Deep Neural Network for Short-term Load Forecasting” at Soongsil University hosted by Prof. Sung-Guk Yoon.
✓ Jun. 2017. Prof. Hongseok Kim gave a talk “Artificial Intelligence and Power Technologies” at Korea Aerospace University hosted by Prof. Jaehwan Lee.
✓ May 2017. The paper “Multiple Power Line Outage Detection in Smart Grids: A Probabilistic Bayesian Approach” has been accepted for IEEE Access.
✓ Apr. 2017. The paper entitled “Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids” has been accepted for Energies.
✓ Apr. 2017. The paper entitled “Downscaling method of target geometries with minimum distortions on statistical features of radar cross sections for 77-GHz automotive radars” has been accepted for Microwave and Optical Technologies Letters.
✓ Mar. 2016. Prof. Hongseok Kim gave a talk “Artificial Intelligence and Power Technologies” at Hyosung CTO R&DE Center.
✓ Mar. 2017. The paper “Robust Operation of Energy Storage System with Uncertain Load Profiles” has been accepted for Energies.
✓ Feb 2017. Prof. Hongseok Kim gave a talk “Optimal Operation of PV and ESS for Energy Prosumers” at 산업교육연구소 for 170+ audiences.
✓ Feb. 2017. The paper entitled “Coalition-based Bidding Strategies for Integrating Renewable Energy Sources in Electricity Market” has been accepted for IEEE Power and Energy Society – General Meeting (PES-GM) 2017!
✓ Jan 2017. Sunghwan Bae and Prof. Hongseok Kim gave a talk “Robust Cooperative Sensing with ON/OFF Signaling over Imperfect Reporting Channels,” at Coding and Information Theory Workshop (부호및정보이론연구회).
✓ Dec 2016. Prof. Hongseok Kim gave a talk “Optimal Operation of PV and ESS for Energy Prosumers” at 산업교육연구소 for 270+ audiences.
✓ Dec. 2016. The paper entitled “Deep Neural Network Based Demand Side Short Term Load Forecasting” has been accepted for Energies.
✓ Dec. 2016. Prof. Hongseok Kim received the Haedong Young Professor Award from Haedong Science Cultural Foundation.
✓ Dec. 2016. The paper entitled “ON/OFF Reporting Mechanism for Robust Cooperative Sensing in Cognitive IoT Networks” has been accepted for IEEE WCNC 2017.
✓ Nov. 2016. The paper entitled “Green Small Cell Operation of Ultra Dense Networks Using Devices Assistance” has been accepted for Energies.
✓ Nov. 2016. The paper entitled “Data-driven Battery Degradation Model Leveraging Average Degradation Function Fitting” has been accepted for Electronics Letters.
✓ Sep. 2016. The paper entitled “On Optimal Cell Flashing for Reducing Delay and Saving Energy in Wireless Networks” has been accepted for Energies.
✓ Sep. 2016. The paper entitled “Robust Cooperative Sensing using ON/OFF Signaling over Imperfect Reporting Channels” has been accepted for IEEE Transactions on Industrial Informatics (TII).
✓ July. 2016. The paper entitled “Deep Neural Network Based Demand Side Short Term Load Forecasting” has been accepted for IEEE Int’l Conf. Smart Grid Comm 2016.
✓ June 2016. Prof. Hongseok Kim gave a talk “ESS and Microgrid Optimization” at Doosan Technical Forum.
✓ June 2016. Prof. Hongseok Kim gave a talk “Artificial Intelligence and Electric Load Forecasting” at Big Data and Power/IT Workshop.
✓ June 2016. The paper “Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-out Cost” has been accepted for Energies.
✓ Apr. 2016. Prof. Hongseok Kim serves on the TPC of IEEE Globecom 2016.
✓ Apr. 2016. Prof. Hongseok Kim gave a talk “Energy IoT” on Future Communications Technologies Workshop.
✓ Mar. 2016. The paper entitled “RCS-based Target Recognition with Real FMCW Radar Implementation” has been accepted for Microwave and Optical Technologies Letters.
✓ Jan. 2016. The paper entitled “Self-Organizing Spectrum Breathing and User Association for Load Balancing in Wireless Networks ” has been accepted for IEEE Trans. on Wireless Communications (TWC).
✓ Sep. 2015. The paper entitled “Data-Driven Baseline Estimation of Residential Buildings for Demand Response” has been published in Energies 2015.
✓ Sep. 2015. The paper entitled “BRUTE: Energy-efficient User Association in Cellular Networks from Population Game Perspective” has been accepted for IEEE Trans. on Wireless Communications (TWC).
✓ Sept. 2015. Prof. Hongseok Kim became an Associate Professor.
✓ Apr. 2015. ISRC has launched with Nice Lab.
✓ Nov. 2014. Yohwan Choi Ph.D. student became a recipient of Ji-kyung Scholarship. Congratulations to Yohwan!
✓ Oct. 2014. The paper “Green Small Cell Operation Using Belief Propagation in Wireless Networks” has been accepted for IEEE Globecom Workshop on Heterogeneous and Small Cell Networks Workshop 2014.
✓ Oct. 2014. The paper entitled “A QoE-aware Proportional Fair Resource Allocation for Multi-cell OFDMA Networks ” has been accepted for IEEE Communications Letters.
✓ Aug. 2014. The paper “QoE-aware Scheduling for Sigmoid Optimization in Wireless Networks ” has been accepted for IEEE Communications Letters.
✓ July. 2014. The paper “A Framework for Baseline Load Estimation in Demand Response: Data Mining Approach” has been accepted for IEEE Smart Grid Comm 2014.
✓ Jan. 2014. Prof. Hongseok Kim serves on the Editor of IEIE Journal of Telecommunications.
✓ Jan. 2014. The paper entitled “Unlimited Cooperative Sensing with Energy Detection for Cognitive Radio” has been accepted for Journal of Communications and Networking.
✓ Nov. 2013. Prof. Hongseok Kim serves on the TPC of IEEE Globecom 2014
✓ Sep. 2013. Prof. Hongseok Kim serves on the TPC of IEEE INFOCOM Workshop on Green Cognitive Communications and Computing Networks (GCCCN) 2014
✓ June. 2013. The paper “Delaunay Triangulation Based Green Base Station Operation for Self Organizing Network” has been accepted for IEEE GreenCom 2013.
✓ Mar. 2013. Prof. Hongseok Kim serves on the TPC of APCC 2013.
✓ Mar. 2013. The paper entitled “Energy-efficient User Association in Cellular Networks: A Population Game Approach” has been accepted for WiOPT 2013.
✓ Feb. 2013. The paper “Spectrum Breathing and User Association for Load Balancing in Wireless Networks” has been accepted for IEEE ICC Small Cell Workshop 2013.
✓ Sep. 2012. The paper “Iterative Resource Pooling for Bandwidth Allocation in TDM-PON: Algorithm, Convergence and Experimental Evaluation” has been published in Photonic Network Communications.
✓ Sep. 2012. Prof. Hongseok Kim serves on the TPC of IEEE ICC 2013 SAC.
✓ Sep. 2012. Prof. Hongseok Kim serves on the TPC of IEEE Globecom 2013 WC.
✓ June 2012. The paper entitled “SeDAX: A Secure, Scalable, Resilient Platform for Smart Grid Communications” has been published in IEEE Journal of Selected Area in Communications (JSAC).
✓ May. 2012. The paper entitled “A Pricing-based Decentralized Spectrum Access Control in Cognitive Radio Networks” has been accepted for IEEE/ACM Transactions on Networking (TON).
✓ Feb. 2012. The paper entitled “Distributed Alpha-Optimal User Association and Cell Load Balancing in Wireless Networks” has been published in IEEE/ACM Transactions on Networking (TON).
✓ Sep. 2011. The paper entitled “Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks” has been published IEEE Journal on Selected Areas in Communications (JSAC).
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