Yuanjun Guo 

PhD/Associate Professor

Shenzhen Institute of Advanced Technology
Chinese Academy of Sciences

Research Areas

My research mainly focus on data-driven modeling methods, big data analysis, deep learning neural network and many other popular artificial intelligence methods, including principal component analysis, input variable selection, neural network multi-layer model structure rapid selection method and intelligent optimization algorithms, with applications in smart grid fault diagnosis, optimal scheduling, load prediction and other aspects of new energy power system, etc.


2011-09--2015-07   Queen's University of Belfast PhD
2008-09--2011-06   Chongqing University Msc
2004-09--2008-06   Chongqing University Bsc


Work Experience

2019-11~Untill now, Associate professor, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences

2015-09~2019-11, Assistant professor, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences



1. Y Guo, Z Yang, K Liu, Y Zhang, W, A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system, Energy (219), 2021, 119529 (SCI, JCR Q1 IF=7.147

2. Y Guo, K Li, and D M Laverty. Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches. IEEE Transactions on Power Delivery, 2015, 30(6): 2544-2552. (SCI JCR Q1, IF 4.131)

3. Y Guo, K Li, Z Yang and D M Laverty. A novel RBF neural network principal component analysis scheme for PMU-based wide-area monitoring of power systems. Electric Power Systems Research, 2015, 127:197-205.SCI JCR Q2 IF 3.211

4. Y Guo, Z Yang, S Feng and J Hu. Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study, Complexity 2018(1):1-21 (SCI Q2 IF 2.833

5. K. Liu, K Li, Q Peng Y Guo*, and L Zhang, Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management, Complexity 2018: 9642892. (SCI, JCR Q1 IF=2.833

6. Z. Yang, K. Li, Y. Guo*, H. Ma, M. Zheng, Compact Real-valued Teaching-Learning Based Optimization with the Applications to Neural Network Training, Knowledge-Based Systems, 2018, 159: 51-62;SCI, JCR Q1, IF=8.038

7.L Zhang, Q Li, Y Guo* Z Yang and L Zhang. An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods. Sustainability, 2018, 10(12). (SCI JCR Q1 IF=3.251

8.J Zhu, Z Yang , M, Mourshed Y Guo and S Feng. Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches[J]. Energies, 2019, 12(14):2692. (SCI, JCR Q3 IF=3.004

9. Y Wang, Z Yang, M Mourshed, Y Guo, Q Niu, X Zhu, Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method, Energy Conversion and Management, 2019(196), 935-949 (SCI, JCR Q1 IF=9.709

10. L Zhang L , K Li , D Du, Y Guo and Z Yang. A Sparse Learning Machine for Real-Time SOC Estimation of Li-ion Batteries[J]. IEEE Access, 2020, PP(99):1-1. (SCI, JCR Q2 IF=3.367

11. Z Yang, K. Li, Y. Guo, S. Feng, Q. Niu, Y Xue, A. Foley, A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles, Energy, 2019, 170: 889-905 (SCI, JCR Q1, IF: 6.082);


Cooperative universities: 

Overseas: University of Leeds, Queen's University Belfast, Cardiff University, etc. 

Mainland: Northeast Dianli University, North China Electric Power University, Harbin Institute of Technology, Shanghai University, Zhengzhou University;  Taiyuan institute of technology,

Cooperative Enterprises: 

State Grid, China Southern Power Grid, CHINA GUANGDONG NUCLEAR POWER GROUP, Shenzhen City Security Institute, etc