Name：Renzhi Lu
Title：Lecturer
Email：lofky7@gmail.com
Website：http://imds/aia.hust.edu.cn
Curriculum Vitae：Now, I am an Assistant Professor in School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, China. Previously, I received my Ph.D. degree (supervised by Prof. Seung Ho Hong) in Division of Electrical Engineering, Hanyang University, Korea, June 2019, and the B.S. degree (supervised by Prof. Guofa Hao and Prof. Jun Yang) from Wuhan University of Science and Technology, China, June 2014.
Research Interests
Artificial Intelligence
(focus on learning algorithm and its application in smart grid and smart manufacturing)
1. Reinforcement Learning/Deep Reinforcement Learning for Home/Commercial/Industrial Energy Management
2. Deep Learning for Load and Price Forecasting
3. Multi-Agent Reinforcement Learning for Cooperative/Competitive/Mixed Cooperative-Competitive System
Smart Grid/Power System
(focus on optimal use of energy resources, analysis and optimization of energy processes)
1. Demand Response, Energy Management
2. Load Forecasting, Price Forecasting
3. Design, Implementation and Evaluation of Power Systems
Smart Manufacturing/Industrial 4.0
(focus on systems design and implementation)
l. Cyber Physical System (CPS), Digital Twin (DT)
2. OPC Unified Architecture (OPC UA), AutomationML (AML), Administration Shell (AAS)
Publications
1.Lu R, Hong S H, Yu M. Demand Response for Home Energy Management using Reinforcement Learning and Artificial Neural Network. IEEE Transactions on Smart Grid, 2019. (Impact Factor: 10.486)
2.Lu R, Hong S H. Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy, 2019, 236: 937-949. (Impact Factor: 8.426)
3.Lu R, Hong S H, Zhang X. A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach. Applied Energy, 2018, 220: 220-230. (Impact Factor: 8.426)
4.Yu M,Lu R, Hong S H. A real-time decision model for industrial load management in a smart grid. Applied Energy, 2016, 183: 1488-1497. (Impact Factor: 8.426)
5.Lu R, Hong S H, Zhang X, et al. A Perspective on Reinforcement Learning in Price-Based Demand Response for Smart Grid. 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017: 1822-1823.
6.Luo Z, Hong S H,Lu R, et al. OPC UA-Based Smart Manufacturing: System Architecture, Implementation, and Execution. Enterprise Systems (ES), 2017 5th International Conference on. IEEE, 2017: 281-286.
7.Ding Y, Hong S H,Lu R, et al. Experimental investigation of the packet loss rate of wireless industrial networks in real industrial environments. Information and Automation, 2015 IEEE International Conference on. IEEE, 2015: 1048-1053.
Professional Services
2018- Member, IEEE Membership
2018- Member, IEEE Computational Intelligence Society Membership
2018- Member, IEEE Industrial Electronics Society Membership
2018- Member, IEEE Power & Energy Society Membership
2015- Reviewer, IEEE Transactions on Industrial Electronics
2015- Reviewer, Applied Energy
2016- Reviewer, IEEE Transactions on Smart Grid
2016- Reviewer, IEEE Transactions on Industrial Informatics
2018- Reviewer, IET Renewable Power Generation
2019- Reviewer, IEEE Transactions on Neural Networks and Learning Systems
