• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Chen, Lei (Chen, Lei.) | Li, Xiaoli (Li, Xiaoli.) | Wang, Kang (Wang, Kang.) | Yu, Xiaowei (Yu, Xiaowei.)

收录:

EI Scopus

摘要:

As of 2018, China had more than 550,000 data centers, accounting for approximately 1.5% of the country's total annual electricity consumption. Reducing energy consumption not only lowers costs but also aligns with environmental protection principles. Current research primarily focuses on IT equipment and air conditioning and refrigeration systems, as these components offer significant potential for energy savings. Instead of making adjustments to the original space environment and hardware facilities of the data center, this study proposes an energy efficiency optimization model by regulating the set values and parameters of the cooling system. This approach is particularly valuable given the increasing emphasis on energy efficiency in data centers. The cooling system of a data center is highly complex, with multiple variables, high feature dimension, time-varying working conditions, and strong uncertainty. This study employs an artificial neural network to model the energy efficiency (PUE) of a data center, using data collected from the operation of the cooling system in a data center in southwest China. Subsequently, an evolutionary algorithm is used to analyze and optimize the cooling system, controlling the set values to enhance the energy efficiency of the data center. © 2024 IEEE.

关键词:

Green computing Energy utilization Cooling systems Air conditioning Neural networks Energy efficiency Thermoelectric equipment Evolutionary algorithms Refrigeration

作者机构:

  • [ 1 ] [Chen, Lei]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 2 ] [Li, Xiaoli]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 3 ] [Wang, Kang]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 4 ] [Yu, Xiaowei]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

年份: 2024

页码: 76-81

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

归属院系:

在线人数/总访问数:464/4953860
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司