严珂，目前为湖南大学教授，博士生导师。曾获国家高层次人才项目，国家级外专项目，以及多项新加坡教育部TIER 1项目。曾任新加坡国立大学高级讲师（2023年QS全球排名第八，专业全球排名第六），日本早稻田大学客座教授，天津城建大学客座教授等。在2020至2023年连续入选全球最具影响力科学家2%榜单，并从2022年起入选全球最具影响力2%科学家终身榜单。担任多个国际知名期刊的副编辑和客座编辑，包括IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS，BUILDING AND ENVIRONMENT，APPLIED ENERGY等等。发表各级别论文百余篇，其中，论文发表在CS顶级会议AAMAS，AAAI，MFCS，能源领域顶级期刊APPLIED ENERGY，RENEWABLE ENERGY，建筑领域顶级期刊ENERGY AND BUILDINGS，BUILDING AND ENVIRONMENT，IEEE TRANSACTIONS TIER1级别期刊IEEE TRANSACTIONS ON SUSTAINABLE ENERGY，INDSUTRIAL INFORMATICS，INTERNET OF THINGS JOUNAL，AUTOMATION SCIENCE AND ENGINEERING等，被引次数超过4000次，H-index为40.

Email：keddiyan@gmail.com

Full Professor, Mechanical and Electrical Engineering, Hunan University. Jan 2023 - Present

Deputy Program Director, Cross-Disciplinary Degree Programme, NUS. Aug 2020 – May 2023

Visiting Professor, Waseda University, Japan. July 2019 - Present

Senior Lecturer, National University of Singapore. Feb 2022 - May 2024

Assistant Professor, Tenure-track, National University of Singapore. Dec 2017 - Jan 2022

Associate Professor, China Jiliang University, Hangzhou, China. Sep 2015 - Dec 2017

Post-Doc Researcher, Masdar Institution, Abu Dhabi, UAE. Mar 2013 - Dec 2014

Research Assistant, National University of Singapore, Singapore. Sep 2010 - Sep 2012

Teaching Assistant, National University of Singapore, Singapore. Jan 2008 - Aug 2010

[1]Spatial embedding-based anomaly pattern detection for high-dimensional time series data,科技部-外国专家交流项目，Jan. 2022 - Dec. 2023

[2]Ministry of Education (MoE) AcRF Tier 1 Funding of Singapore. Dec 2021 - Nov 2024

Title: AI and IoT Technologies based Automatic FDD Methods with Completely No Actual Fault Data using GAN and EnergyPlus Models (No. R-296-000-241-114).

Budget: 174,000 Singapore dollar.

[3]Ministry of Education (MoE) AcRF Tier 1 Funding of Singapore. Feb 2020 - Jan 2023

Title: The Internet-of-Buildings (IoB) Platform – Visual Analytics for AI Technologies towards a Well and Green Built Environment (No. R-296-000-214-114).

Budget: 200,000 Singapore dollar.

[4]Ministry of Education (MoE) AcRF Tier 1 Funding of Singapore. Sep 2019 - July 2022

Title: Semi-supervised Learning and Unsupervised Learning Methods in Fault Detection and Diagnosis of HVAC Subsystems (No. R-296-000-208-133).

Budget: 171,000 Singapore dollar.

发表论文：

HVAC FDD

[1]Jian Bi, Hua Wang, Enbo Yan, Chuan Wang, Ke Yan, Liangliang Jiang, Bin Wang. "AI in HVAC Fault Detection and Diagnosis: A Systematic Review." Energy Reviews, ENREV-D-23-00050, 2024.

[2]Yan, Ke, and Xiaokang Zhou. "Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN." Digital Communications and Networks 8, no. 4 (2022): 531-539.

[3]Yan, Ke. "Chiller fault detection and diagnosis with anomaly detective generative adversarial network." Building and Environment 201 (2021): 107982.

[4]Yan, Ke, Adrian Chong, and Yuchang Mo. "Generative adversarial network for fault detection diagnosis of chillers." Building and Environment 172 (2020): 106698.

[5]Yan, Ke, Jing Huang, Wen Shen, and Zhiwei Ji. "Unsupervised learning for fault detection and diagnosis of air handling units." Energy and Buildings 210 (2020): 109689.

[6]Yan, Ke, Jianye Su, Jing Huang, and Yuchang Mo. "Chiller fault diagnosis based on VAE-enabled generative adversarial networks." IEEE Transactions on Automation Science and Engineering 19, no. 1 (2020): 387-395.

[7]Yan, Ke, Zhiwei Ji, Huijuan Lu, Jing Huang, Wen Shen, and Yu Xue. "Fast and accurate classification of time series data using extended ELM: Application in fault diagnosis of air handling units." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 7 (2017): 1349-1356.

[8]Yan, Ke, Cheng Lu, Xiang Ma, Zhiwei Ji, and Jing Huang. "Intelligent fault diagnosis for air handing units based on improved generative adversarial network and deep reinforcement learning."Expert Systems with Applications(2023): 122545.

[9]Yan, Ke, Xinke Chen, Xiaokang Zhou, Zheng Yan, and Jianhua Ma. "Physical model informed fault detection and diagnosis of air handling units based on transformer generative adversarial network."IEEE Transactions on Industrial Informatics19, no. 2 (2022): 2192-2199.

[10]Yan, Ke, Wen Shen, Timothy Mulumba, and Afshin Afshari. "ARX model based fault detection and diagnosis for chillers using support vector machines."Energy and Buildings81 (2014): 287-295.

[11]Mulumba, Timothy, Afshin Afshari, Ke Yan, Wen Shen, and Leslie K. Norford. "Robust model-based fault diagnosis for air handling units."Energy and Buildings86 (2015): 698-707.

[12]Jian Bi, HuaWang, Mei Hua, Ke Yan*. An Interpretable Feature Selection Method Integrating Ensemble Models for Chiller Fault Diagnosis. Journal of Building Engineering, 87: 109029, 2024.

[13]Mei Hua, Ke Yan*, Jian Bi, Hua Wang. SS-CWGAN: A novel fault diagnosis model for building HVAC systems under limited labeled data. Energy and Buildings, 319: 114540, 2024.

[14]Hua Wang, Jian Bi, Mei Hua, Ke Yan*, and Afshin Afshari. "Semi-supervised CWGAN-GP modeling for AHU AFDD with high-quality synthetic data filtering mechanism."Building and Environment(2024): 112265.

Air Quality Modeling and Forecasting

[1]Jin, Ning, Yongkang Zeng, Ke Yan, and Zhiwei Ji. "Multivariate air quality forecasting with nested long short term memory neural network." IEEE Transactions on Industrial Informatics 17, no. 12 (2021): 8514-8522.

[2]Zhang, Zhendong, Yongkang Zeng, and Ke Yan. "A hybrid deep learning technology for PM 2.5 air quality forecasting."Environmental Science and Pollution Research28 (2021): 39409-39422.

Building Energy Consumption Modeling, Forecasting and Optimization

[1]Yan, Hainan, Ke Yan, and Guohua Ji. "Optimization and prediction in the early design stage of office buildings using genetic and XGBoost algorithms." Building and Environment 218 (2022): 109081.

[2]Yan, Ke, Xiaokang Zhou, and Jinjun Chen. "Collaborative deep learning framework on IoT data with bidirectional NLSTM neural networks for energy consumption forecasting." Journal of Parallel and Distributed Computing 163 (2022): 248-255.

[3]Yang, Fan, Ke Yan, Ning Jin, and Yang Du. "Multiple households energy consumption forecasting using consistent modeling with privacy preservation." Advanced Engineering Informatics 55 (2023): 101846.

[4]Zhang, Bidan, Yang Du, Xiaoyang Chen, Eng Gee Lim, Lin Jiang, and Ke Yan. "A novel adaptive penalty mechanism for Peer-to-Peer energy trading."Applied Energy327 (2022): 120125.

[5]Jin, Ning, Fan Yang, Yuchang Mo, Yongkang Zeng, Xiaokang Zhou, Ke Yan, and Xiang Ma. "Highly accurate energy consumption forecasting model based on parallel LSTM neural networks."Advanced Engineering Informatics51 (2022): 101442.

[6]Li, Tingting, Yang Zhao, Ke Yan, Kai Zhou, Chaobo Zhang, and Xuejun Zhang. "Probabilistic graphical models in energy systems: A review." InBuilding Simulation, pp. 1-30. Tsinghua University Press, 2021.

[7]Anand, Prashant, Chirag Deb, Ke Yan, Junjing Yang, David Cheong, and Chandra Sekhar. "Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings."Energy and Buildings252 (2021): 111478.

[8]Yan, Ke, Wei Li, Zhiwei Ji, Meng Qi, and Yang Du. "A hybrid LSTM neural network for energy consumption forecasting of individual households."Ieee Access7 (2019): 157633-157642.

[9]Jian Liu, Fan Yang, Ke Yan*, and Liangliang Jiang. "Household Energy Consumption Forecasting based on Adaptive Signal Decomposition Enhanced iTransformer Network."Energy and Buildings(2024): 114894.