Xiangyu Zhang joined the Complex System Simulation and Optimization Group within the Computational Science Center at NREL in February 2019. At NREL, Xiangyu’s main research interest focuses on using optimization techniques and high-performance computing to improve the policy search in deep reinforcement learning (RL). The overarching objective is to enable training of reliable RL controllers for emerging stochastic nonlinear control problems in the energy systems in an efficient, practical, and cost-effective manner.
Before joining NREL, Xiangyu received his doctorate from Virginia Tech where he conducted research on cost-effective, intelligent building-grid integrated control via Internet-of-Things technologies. Even before that, he studied the mechanism, prevention, and mitigation of power system cascading failure/blackout at Tsinghua University.
For additional information, see Xiangyu Zhang's LinkedIn profile.
Disclaimer: Any opinions expressed on LinkedIn are the author’s own, made in the author's individual capacity, and do not necessarily reflect the views of NREL.
Reinforcement learning for energy system optimal control
Smart grid and smart buildings
Ph.D., Electrical Engineering, Virginia Tech
M.S., Electrical Engineering, Tsinghua University
B.S., Electrical Engineering, Wuhan University
Postdoctoral Researcher, NREL (2019–present)
Software Developer Summer Intern, Google Nest (2018)
Graduate Research Assistant, Virginia Tech (2015–2018)
Graduate Teaching Assistant, Virginia Tech (2014–2015)
Associations and Memberships
Member, Institute of Electrical and Electronics Engineers (IEEE)
An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning, IEEE Transactions on Smart Grid (2020)
An IoT-Based Thermal Model Learning Framework for Smart Buildings, IEEE Internet of Things Journal (2019)
A Self-Learning Algorithm for Coordinated Control of Rooftop Units in Small-and Medium-Sized Commercial Buildings, Applied Energy (2017)
Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM, NREL Conference Paper Preprint (2020)
Transferable Reinforcement Learning for Smart Homes, Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (2020)
Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning, NREL Conference Paper Preprint (2020)