Machine learning assisted development of IT equipment compact models for data centers energy planning
Machine learning assisted development of IT equipment compact models for data centers energy planning
复制标题
DOI：
10.1016/j.apenergy.2021.117846
复制
发表时间：
2022-01
期刊：
影响因子：
11.2
通讯作者：
Yaman M. Manaserh;Mohammad I. Tradat;Dana Bani-Hani;Aseel Alfallah;B. Sammakia;K. Nemati;M. Seymour-M.-Seym
中科院分区：
文献类型：
--
作者：
Yaman M. Manaserh;Mohammad I. Tradat;Dana Bani-Hani;Aseel Alfallah;B. Sammakia;K. Nemati;M. Seymour-M.-Seym
在大多数数据中心中，通常通过将冷却单元提供的气流量设置为大大超过IT设备所需的气流量来确保性能可靠性。这种过于保守的策略需要额外的能源支出，这不可避免地导致大量的能源被冷却系统浪费。为了避免采用这种浪费的策略，对气流、温度和能源进行适当的管理至关重要。为此，这项工作提出了一种新的方法来开发一个紧凑的IT设备模型在非设计条件。该模型旨在支持数据中心的热量和能源管理功能。该模型的好处是，它不仅可以准确地预测IT设备的功耗，还可以预测设备所需的流量和离开设备的空气温度。虽然紧凑型模型的功耗是作为CPU利用率的函数导出的，但其流量需求和排气温度是从动态详细CFD模型获得的。通过实验验证了该模型的正确性，最大不匹配度为出口温度场的5.7%和流量的11.4%。与最先进的IT设备紧凑型模型相比，所开发的模型被发现可以将设备的流量和出口空气温度的预测误差分别降低到最先进的IT设备紧凑型模型的5.2%和9.3%。
In most data centers, performance reliability is often ensured by setting the amount of airflow provided by the cooling units to substantially exceed that which is needed by the IT equipment. This overly conservative strategy requires additional energy expenditure, which inevitably results in a huge amount of energy being wasted by the cooling system. To eliminate adopting such wasteful policies, conducting proper management of airflow, temperature, and energy is critical. To that end, this work proposes a novel approach to developing a compact IT equipment model at off-design conditions. This model is designed to support thermal and energy management functions in data centers. The benefit of this model is that it can accurately predict not only the IT equipment power consumption, but also the amount of flowrate required for the equipment and the air temperature leaving the equipment. While the compact model’s power consumption was derived as a function of CPU utilization, its flowrate demand and exhaust temperature were obtained from a dynamic detailed CFD model. Results from the compact model were validated with experiments where the maximum mismatch was found to be 5.7% in the outlet temperature field and 11.4% in flowrate. Compared to a state-of-the-art IT equipment compact model, the developed model was found to reduce the prediction error of the equipment’s flowrate and outlet air temperature by up to 5.2% and 9.3 % that of the state-of-the-art IT equipment compact model, respectively.