A time-varying state-space model for real-time temperature predictions in rack-based cooling data centers

Tong, Xiaojiao; Wang, Jiaqiang; Liu, Weiwei; Chang, Youngho; Zhang, Quan; Zhang, Linfeng

doi:10.1016/j.applthermaleng.2023.120737

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…Especially in the same data center server room, multiple refrigeration units "go their own way" and lack the intelligence of group control, which results in a waste of refrigeration resources and increases the overall energy consumption of the refrigeration system. AI control method can solve the above problems, unlike the basic control method, the latter adopts the intelligent collector in addition to collecting a large number of temperature data but also collects the energy consumption data of refrigeration equipment, power supply and distribution equipment, and IT information equipment for background management [10][11][12][13][14][15]. The AI control approach is based on AI technology, combining physical a priori, big data and IoT technology, with the help of historical data, real-time data, and algorithmic models, etc., to predict potential risks, optimize resource allocation, and achieve the purpose of predicting temperatures, intelligent management, as well as reducing energy consumption [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

Huang,

Li,

et al. 2023

Energies

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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.

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