Professor Phone: +86 15871800142 Email: firstname.lastname@example.org Academic Areas: Hydropower Engineering Research Interests:Control of Hydropower, wind power and other clean energy sets; Condition-maintenance of power generation equipment; Forecasting and planning of multiple source energy system; Applications of Artificial intelligence. Academic Degrees PhD in Hydropower Engineering, 2010, Huazhong University of science and technology; BA in Thermal energy and Power Engineering (hydraulic direction), 2005, WuHan University. Professional Experience Professor (2012-present); Huazhong University of science and technology; Associate Professor (2012-2016); Huazhong University of science and technology; Assistant Professor (2010-2012); Huazhong University of science and technology. Selected Publications Yuying Xie, Chaoshun Li*, Geng Tang, Zhenhao Gan. A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting. Energy, 2021, 216: 119179. Bo Fu, Wangjun Yuan, Xiaolong Cui, Tian Yu, XiLin Zhao, Chaoshun Li*. Correlation Analysis and Augmentation of Samples for a Bidirectional Gate Recurrent Unit for the Remaining Useful Life Prediction of Bearings, IEEE Sensors Journal, 2020, DOI 10.1109/JSEN.2020.3046653. Xiaosheng Peng, Hongyu Wang, Jianxun Lang, Wenze Li, Qiyou Xu, Zuowei Zhang, Tao Cai, Shanxu Duan1, Fangjie Liu, Chaoshun Li*. EALSTM-QR: a new interval wind power forecasting model using numerical weather prediction and deep learning technique. Energy, 2020, doi.org/10.1016/j.energy.2020.119692. YizhuoMa, ChaoshunLi*, JianzhongZhou, YongchuanZhang. Comprehensive stochastic optimal scheduling in residential micro energy grid considering pumped-storage unit and demand response. Journal of Energy Storage, 2020, 32: 101968. Ruoheng Wang, Chaoshun Li*, Wenlong Fu*, Geng Tang. A Deep Learning Method based on GRU and VMD for Short-term Wind Power Interval Prediction. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31 (10): 3814 - 3827. Xiaolong Cui, Chaoshun Li*, Bailin Li, Yi Li. Instantaneous Feature Extraction and Time-Frequency Representation of Rotor Purified Orbit Based on Vold-Kalman Filter. IEEE Transactions on Instrumentation & Measurement, 2020, 69(10): 7386-7397. Geng Tang, Yifan Wu, Chaoshun Li*, Pak Kin Wong, Zhihuai Xiao, Xueli An. A Novel Wind Speed Interval Prediction based on Error Prediction Method. IEEE Transactions on Industrial Informatics, 2020,16 (11): 6806-6815. Chaoshun Li*, Geng Tang, Xiaoming Xue, Adnan Saeed, Xin Hu. Short-term Wind Speed Interval Prediction based on Ensemble GRU model. IEEE Transactions on Sustainable Energy, 2020,11 (3):1370-1380. Chaoshun Li*, Geng Tang, Xiaoming Xue*, Xinbiao Chen, Ruoheng Wang, Chu Zhang. The short-term interval prediction of wind power using the deep learning model with gradient descend optimization. Renewable Energy, 2020, 155: 197-211. Xinjie Lai, Chaoshun Li*, Jianzhong Zhou, Yongchuan Zhang, Yonggang Li. A multi-objective optimization strategy for the optimal control scheme of pumped hydropower systems under successive load rejections. Applied Energy, 2020, 261,114474. Chaoshun Li*, Wenxiao Wang, Jinwen Wang, Deshu Chen. Network-constrained unit commitment with RE uncertainty and PHES by using a binary artificial sheep algorithm. Energy, 2019, 189 : 116203. Xinjie Lai, Chaoshun Li*, Jianzhong Zhou. A Multi-objective Artificial Sheep Algorithm. Neural Comput & Applic, 2019, 31(8): 4049–4083. Xinjie Lai, Chaoshun Li*, Wencheng Guo*, Yanhe Xu, Yonggang Li. Stability and dynamic characteristics of the nonlinear coupling system of hydropower station and power grid. Communications in Nonlinear Science and Numerical Simulation, 2019, 79: 104919. Wen Zou, Chaoshun Li*, Pengfei Chen. An Inter Type-2 FCR Algorithm Based T-S Fuzzy Model for Short-term Wind Power Interval Prediction. IEEE Transactions on Industrial Informatics, 2019, 15(9): 4934 – 4943. Wenlong Fu*, Kai Wang, Chaoshun Li*, Jiawen Tan. Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM. Energy Conversion and Management, 2019, 187:356-377. Xinjie Lai, Chaoshun Li*, Jianzhong Zhou, Nan Zhang. Multi-objective optimization of the closure law of guide vanes for pumped storage units. Renewable Energy, 2019, 139:302-312. Chaoshun Li*, Wenxiao Wang, Deshu Chen. Multi-objective complementary scheduling of Hydro-Thermal-RE power system via a multi-objective hybrid grey wolf optimizer. Energy, 2019, 171: 241-255. Chaoshun Li*, Zhengguang Xiao, Xin Xia*, Wen Zou, Chu Zhang. A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting. Applied Energy, 2018,215:131–144. Wen Zou, Chaoshun Li*, Nan Zhang. A T-S Fuzzy Model Identification Approach based on a Modified Inter Type-2 FRCM Algorithm. IEEE Transactions on Fuzzy Systems, 2018, 26(3): 1104 – 1113. Chaoshun Li*, Zhou J, Chang L, et al. T-S fuzzy model identification based on a novel hyper-plane-shaped membership function. IEEE Transactions on Fuzzy Systems, 2017, 25 (5), 1364-1370. Chaoshun Li*, Yifeng Mao, Jiandong Yang, et al. A nonlinear generalized predictive control for pumped storage unit. Renewable Energy, 2017, 114:945-959. Chaoshun Li*, Nan Zhang, Xinjie Lai, Jianzhong Zhou, Yanhe Xu. Design of a fractional order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation. Information Sciences, 2017, 396: 162–181. Wenxiao Wang, Chaoshun Li*, Xiang Liao, Hui Qin. Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm. Applied Energy, 2017, 187: 612–626. Chaoshun Li*, Yifeng Mao, Jianzhong Zhou, Nan Zhang, Xueli An. Design of a fuzzy-PID controller for a nonlinear hydraulic turbine governing system by using a novel gravitational search algorithm based on Cauchy mutation and mass weighting. Applied Soft Computing, 2017, 52: 290-305. Meng Luo, Chaoshun Li*, Xiaoyuan Zhang, Ruhai Li, Xueli An. Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings. ISA Transactions, 2016, 65: 556-566. Chaoshun Li*, Li Chang, Zhengjun Huang, et al. Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2016, 50: 177-191. Nan Zhang, Chaoshun Li*, Ruhai Li, Xinjie Lai, Yuanchuan Zhang. A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system. Knowledge-Based Systems, 2016, 109: 218-237. Chaoshun Li*, Jianzhong Zhou. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis. ISA Transactions, 2014, 53(5): 1534-1543. Chaoshun Li*, Jianzhong Zhou, Bo Fu, Pangao Kou, Jian Xiao, T-S fuzzy model identification with gravitational search based hyper-plane clustering algorithm, IEEE Transactions on Fuzzy Systems, 2012, 20 (2): 305-317. Chaoshun Li, Jianzhong Zhou*, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm, Energy Conversion and Management, 2011, 52 (1), 374-381. Chaoshun Li, Jianzhong Zhou*, Qingqing Li, et.al. A new T-S fuzzy-modeling approach to identify a boiler–turbine system. Expert Systems with Applications, 2010, 37(3): 2214-2221. Chaoshun Li, Jianzhong Zhou*, Xiuqiao Xiang, Qingqing Li, Xueli An. T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm. Engineering Applications of Artificial Intelligence, 2009, 22(4-5): 646-653. Courses Taught Synchronous generator excitation system in hydropower station (for undergraduate student) Condition-based maintenance for hydropower generating units (for graduate student) Project National talent project, 2019. Fault diagnosis and performance prediction of large hydropower units based on deep learning, granted by Hubei outstanding youth fund, 2019. Fault knowledge mapping construction and uncertain reasoning diagnosis of hydropower units integrated with deep learning, granted by National Natural Science Foundation of China, 2018. Fault knowledge mapping construction and reasoning diagnosis of smart grid equipment integrating deep learning, granted by Wuhan Science and Technology Bureau, 2018. Research on multi-scale control of pumped storage wind solar hybrid intelligent microgrid, granted by National Natural Science Foundation of China, 2016. Research on integrated fault diagnosis and nonlinear predictive control of pumped storage units, granted by National Natural Science Foundation of China, 2014. Research on fault diagnosis method of pumped storage unit control system based on fuzzy identification and multi model description, granted by National Natural Science Foundation of China, 2011. Research on complex feature identification and fault diagnosis of hydropower unit control system based on fuzzy multi model, granted by Ministry of education of China, 2011. Awards Electric Power Construction Science and Technology award (1st Grad, ranked second), 2020, Degradation analysis technology and application of hydropower units based on data driven, China Power Construction Association. Excellent Scientific Research Achievement Award of colleges and universities (NATURAL SCIENCE) (1st Grad, ranked second), 2017, Dynamic modeling, fault diagnosis and optimal control of large hydropower units, Ministry of Education of China. Hydropower Science and Technology Award (1st Grad, ranked second), 2015, Key technology and application of fault diagnosis and optimal control for large hydropower units, China Society of Hydropower Engineering. China University Press Book Award (excellent academic works), 2015, Principles and methods of hydro generator dynamic problems and fault diagnosis (co-author), China University Press Association. Nomination of National Excellent Doctoral Dissertation Award, 2012, Research on control system identification and fault diagnosis of hydropower units, State Academic Degree Committee.