Simulation-based assessment of data center waste heat utilization using aquifer thermal energy storage of a university campus

Dvořák, Vojtěch; Zavrel, V Vojtech; Torrens-Galdiz, J. Ignacio; Hensen, Jlm Jan

doi:10.1007/s12273-020-0629-y

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…This field consists of building a model that mimics a real system's behavior based on explicitly defined physical laws and heuristic expressions [77] [81] [17] [49] [14] [21]. If the simulator approaches real-world systems, they provide high-fidelity data.…”

Section: Simulation Based Modelsmentioning

confidence: 99%

Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks

Pérez,

Arroba,

Moya

2022

Preprint

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The Cloud paradigm is at a critical point in which the existing energy-efficiency techniques are reaching a plateau, while the computing resources demand at Data Center facilities continues to increase exponentially. The main challenge in achieving a global energy efficiency strategy based on Artificial Intelligence is that we need massive amounts of data to feed the algorithms. Nowadays, any optimization strategy must begin with data. However, companies with access to these large amounts of data decide not to share them because it could compromise their security. This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. For this purpose, we will implement a powerful generative algorithm: Generative Adversarial Networks (GANs). The use of GANs will allow us to handle multivariate data and data from different natures (e.g., categorical). On the other hand, adapting Data Centers' operational management to the occurrence of sporadic anomalies is complicated due to the reduced frequency of failures in the system. Therefore, we also propose a methodology to increase the generated data variability by introducing

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Section: Simulation Based Modelsmentioning

confidence: 99%

Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks

Pérez,

Arroba,

Moya

2022

Preprint

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“…Academic data centers are typically known to be less efficient. In 2014 Choo et al [10] reported a PUE of 2.73 and in 2020, Dvorak et al [11] reported a PUE of 1.78 for the academic data centers they studied.…”

Section: Introductionmentioning

confidence: 99%

“…In reviewing the literature over the last five years, we found eleven case studies on academic data centers; of these, only five (i.e., [11,[37][38][39][40]) analyzed the reuse of waste heat. In all five studies, the waste heat that can be reused from the academic data center was determined using analytical methods with waste heat temperatures that were calculated based on consumer use of the data center rather than measured in real time.…”

Section: Introductionmentioning

confidence: 99%

“…In all five studies, the waste heat that can be reused from the academic data center was determined using analytical methods with waste heat temperatures that were calculated based on consumer use of the data center rather than measured in real time. Calculated temperatures ranging from 25 to 45 • C for air-and liquid-cooling scenarios [11,37] resulted in annual waste heat quantities ranging from 3156 to 11,045 MWh. District (campus) heating was the only application discussed.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Anthropogenic Waste Heat Emission from an Academic Data Center

Ding,

Ebrahimi,

Kim

et al. 2024

Energies

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The rapid growth in computing and data transmission has significant energy and environmental implications. While there is considerable interest in waste heat emission and reuse in commercial data centers, opportunities in academic data centers remain largely unexplored. In this study, real-time onsite waste heat data were collected from a typical academic data center and an analysis framework was developed to determine the quality and quantity of waste heat that can be contained for reuse. In the absence of a comprehensive computer room monitoring system, real-time thermal data were collected from the data center using two arrays of thermometers and thermo-anemometers in the server room. Additionally, a computational fluid dynamics model was used to simulate temperature distribution and identify “hot spots” in the server room. By simulating modification of the server room with a hot air containment system, the return air temperature increased from 23 to 46 °C and the annual waste heat energy increased from 377 to 2004 MWh. Our study emphasizes the importance of containing waste heat so that it can be available for reuse, and also, that reusing the waste heat has value in not releasing it to the environment.

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“…Thermal environment optimization can affect the building energy use, while building energy use can also react to the thermal environment changes. Heating, ventilating and air-conditioning (HVAC) system is an important part of contemporary building sectors, which directly and significantly affects both indoor thermal environment and energy use (Dvorak et al, 2020). Building sectors are responsible for 20-40% of the total energy consumption in different countries, of which HVAC systems account for 40-60% (Alimohammadisagvand et al, 2016;.…”

Section: Introductionmentioning

confidence: 99%