A review of air conditioning energy performance in data centers

Ni, Jiacheng; Bai, Xian–Xu

doi:10.1016/j.rser.2016.09.050

Cited by 214 publications

(84 citation statements)

References 29 publications

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“…As the basis of MPC, energy forecasting models need to have high fidelity and computationally efficiency in electrical systems of buildings [42]. The energy forecasting models can crucially affect the energy consumptions in HVAC systems [21,43]. To improve energy efficiency, it is critical to optimize system operations in SFM [44].…”

Section: Optimization Approaches For System Efficiency In Sustainablementioning

confidence: 99%

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

2017

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Sustainable facilities management (SFM) opens the door of opportunity for companies to evaluate the quality of resources and environment management at their facilities. It enables the principles of sustainable development. There is still inefficiency in quantitative research of integrating environmental factors, particularly multi-source data, to monitor and control complicated systems in buildings. The objective of this research is to develop an effective method to dynamically optimize energy efficiency in SFM plans and strategies. The research question is: can the integrated proactive method reduce energy consumption with dynamically adjustable controls? This paper proposes a coordinated proactive control method using dynamic time-series prediction (PCM-DTSP) for SFM, which optimizes system controls by integrating the prediction results and monitored environmental-data. The results show that, after optimization, the temperature fluctuations are reduced to 33.3%. The average temperature and maximum temperature are reduced by 8% and 13.1%, respectively. The instantaneous power consumption was reduced by 0.17 KW per hour for each cooling system unit. The PCM-DTSP method can significantly optimize energy efficiency, which paves the way for long-term comprehensive energy management. The contribution of the research lies in its optimized control of energy consumption, temperature stabilization, and improvement of environmental comfort solutions, which can be generalized to various types of buildings.

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Section: Optimization Approaches For System Efficiency In Sustainablementioning

confidence: 99%

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

2017

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“…DC electricity consumption in the U.S. alone is projected to increase to roughly 140 billion kilowatthours annually by 2020, costing $13 billion in electricity bills and emitting nearly 100 million tons of carbon pollution per year [2]. In this paper, we focus on one of the significant Yuanlong sources of energy consumption in DC (about 38% [3]), the cooling energy.…”

Section: Introductionmentioning

confidence: 99%

Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

Wen

Tao

et al. 2020

IEEE Trans. Cybern.

149

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Cooling system plays a critical role in a modern data center (DC). Developing an optimal control policy for DC cooling system is a challenging task. The prevailing approaches often rely on approximating system models that are built upon the knowledge of mechanical cooling, electrical and thermal management, which is difficult to design and may lead to suboptimal or unstable performances. In this paper, we propose utilizing the large amount of monitoring data in DC to optimize the control policy. To do so, we cast the cooling control policy design into an energy cost minimization problem with temperature constraints, and tap it into the emerging deep reinforcement learning (DRL) framework. Specifically, we propose an end-toend cooling control algorithm (CCA) that is based on the actorcritic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm. In the proposed CCA, an evaluation network is trained to predict an energy cost counter penalized by the cooling status of the DC room, and a policy network is trained to predict optimized control settings when given the current load and weather information. The proposed algorithm is evaluated on the EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. Our results show that the proposed CCA can achieve about 11% cooling cost saving on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study, we propose a de-underestimation validation mechanism as we cannot directly test the algorithm on a real DC. Even though with DUE the results are conservative, we can still achieve about 15% cooling energy saving on the NSCC data trace if we set the inlet temperature threshold at 26.6 degree Celsius.

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“…8 The air-cooling methods are traditional DC cooling techniques which circulate the chilled air. 9 However, due to the low heat removal capacity of the air (37 W/cm 2 ), the air-cooled DC systems cannot be energy efficient and economically feasible for the rapidly developed ICT technologies, which have high heat flux; hence, the liquid-cooling DC methods have been being popular thanks to their high heat removal capacities (100 W/cm 2 ) in order to provide affordable and energy-efficient DC cooling operations. 8,10 Another advantage of the liquidcooling DC systems is becoming independent from the ambient air conditions, namely, the changes in air temperature and relative humidity do not affect the system performance as remarkable as the air-cooled DC systems.…”

Section: Introductionmentioning

confidence: 99%

Combined heat and power generation via hybrid data center cooling‐polymer electrolyte membrane fuel cell system

Kanbur

Duan

2020

Int J Energy Res

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Summary Although there has been a lot of waste heat utilization studies for the air‐cooled data center (DC) systems, the waste heat utilization has not been studied for the liquid‐cooled DC systems, which have been rapidly gaining importance for the high‐performance Information and Communication Technology facilities such as cloud computing and big data storage. Compared to the air‐cooled systems, higher heat removal capacity of the liquid‐cooled DC systems provides better heat transfer performance; and therefore, the waste heat of the liquid‐cooled DC systems can be more efficiently utilized in the low‐temperature and low‐carbon energy systems such as electricity generation via polymer electrolyte membrane (PEM) fuel cells. For this purpose, the current study proposes a novel hybrid system that consists of the PEM fuel cell and the two‐phase liquid‐immersion DC cooling system. The two‐phase liquid immersion DC cooling system is one of the most recent and advanced DC cooling methods and has not been considered in the DC waste heat utilization studies before. The PEM fuel cell unit is operated with the hydrogen and compressed air flows that are pre‐heated in the DC cooling unit. Due to its original design, the hybrid system brings its own original design criteria and limitations, which are taken into account in the energetic and exergetic assessments. The power density of the PEM fuel cell reaches up to 0.99 kW/m2 with the water production rate of 0.0157 kg/s. In the electricity generation case, the highest energetic efficiency is found as 15.8% whereas the efficiency increases up to 96.16% when different multigeneration cases are considered. The hybrid design deduces that the highest exergetic efficiency and sustainability index are 43.3% and 1.76 and they are 9.4% and 6.6% higher than exergetic and sustainability performances of the stand‐alone PEM fuel cell operation, respectively.

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A review of air conditioning energy performance in data centers

Cited by 214 publications

References 29 publications

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

Combined heat and power generation via hybrid data center cooling‐polymer electrolyte membrane fuel cell system

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