Energy efficiency optimization method for process parameters of machining system based on reinforcement learning

2021 ◽  
Author(s):  
Chenxun Lu ◽  
Wei Yan ◽  
Feng Ma ◽  
Hua Zhang ◽  
Xumei Zhang
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Process Parameters ◽  
Optimization Method ◽  
Efficiency Optimization ◽  
Machining System

scholarly journals A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy

Energies ◽  
2012 ◽  
Vol 5 (12) ◽  
pp. 5257-5278 ◽  
Author(s):  
Anna Pisello ◽  
Michael Bobker ◽  
Franco Cotana
Keyword(s):  
Energy Efficiency ◽  
Optimization Method ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Efficiency Optimization

scholarly journals Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2071
Author(s):  
Ce Chi ◽  
Kaixuan Ji ◽  
Penglei Song ◽  
Avinab Marahatta ◽  
Shikui Zhang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Resource Utilization ◽  
Data Center ◽  
Data Centers ◽  
Optimization Method ◽  
Comparison Method ◽  
Cooling Systems ◽  
Model Free ◽  
Multi Agent

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.

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