Data-Driven Methods for Sustainable Industrial and AIoT Systems

Last updated on Jun 25, 2024
Generated by Microsoft Designer

Conventional industrial systems and emerging systems such as data centers, 5G communication networks consume enormous amount of energy and non-renewable resources. We focus on developing advanced data-driven AI methods to optimize real-world complex industrial and AIoT systems. Helping the related industries to improve operation efficiency, save energy, reduce emission, and ultimately achieving the goal of green and sustanable development.

Our current research focus include:

  • Simulator-free data-driven control optimization for complex industrial systems
  • Energy saving optimization for data centers
  • 5G Massive MIMO Beamforming optimization
  • Engineering policy integrated hybrid RL
AIoT
Xianyuan Zhan
Xianyuan Zhan
Faculty Member

Publications

TESLA: Thermally Safe, Load-Aware, and Energy-Efficient Cooling Control System for Data Centers
The increasing demand for artificial intelligence and cloud computing has led to skyrocketing energy consumption of data centers (DCs). …
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A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement …
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Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets …
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An Efficient Multi-Agent Optimization Approach for Coordinated Massive MIMO Beamforming
Beamforming plays an important role in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications. Optimizing beamforming …
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DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy …
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