Artificial Intelligence-Based Temperature Twinning and Pre-Control for Data Center Airflow Organization

• Published in: Energies (Basel) 2023-08, Vol.16 (16), p.6063
• Main Authors: Huang, Na, Li, Xiang, Xu, Quanming, Chen, Ronghao, Chen, Huidong, Chen, Aidong
• Format: Article
• Language: English
• Subjects: Air quality management; Artificial intelligence; deep reinforcement learning; digital twin; Emissions (Pollution); Energy consumption; Energy efficiency; long and short-term memory networks; Neural networks; Strategic planning (Business); Sustainable development; temperature prediction
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16166063

Green and low-carbon has become the main theme of global energy development. Data centers are the core of the digital age, carrying huge arithmetic demand. Data centers must implement green low-carbon energy efficiency management to improve energy efficiency, reduce energy waste and carbon emissions, and achieve sustainable development. As a result, an intelligent management strategy for dynamic energy efficiency of data center networks with Artificial Intelligence (AI) fitting control is proposed. Firstly, a Long Short-Term Memory (LSTM) network is used for long sequence trend prediction to predict the temperature of the data center in the next sequence using the temperature of the past 15 sequences and the power consumption of the equipment as parameters. Then, based on the prediction results, the intelligent air conditioning controller based on Deep Q-Network (DQN) is designed to update the parameters by using the gradient of double-Q network and error backpropagation, and the optimal control action is selected by using the ε-greedy strategy to ensure that the prediction of the hotspot does not occur. Experiments show that the average absolute errors of temperature prediction for supply air, return air, cold aisle as well as hot aisle are 0.32 °C, 0.21 °C, 0.36 °C and 0.19 °C, respectively. The Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) decreased by an average of 2.6% and 2.5%, respectively. The method achieves the purpose of predicting future temperatures and intelligently controlling the output so that the data center can satisfy the premise of normal operation and thus achieve more efficient energy use.