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Copyright of Journal of Refrigeration is the property of Journal of Refrigeration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To reduce energy consumption in data center cooling systems and ensure the healthy and rapid development of data centers, this study investigated on the cooling system of a data center in Guangzhou. Aimed at the efficiency variations of chillers at different Partial Load Rates (PLR), a novel model predictive control method was proposed. The study employed Gaussian process regression to establish a chillers' operational efficiency model and utilized Long Short-Term Memory (LSTM) neural networks to create an outdoor wet bulb temperature time series prediction model. The cooling system's chilled water storage and discharge modes, storage and discharge capacity, the number of chillers, and the PLR were adjusted based on the data center cooling load and the prediction of outdoor wet bulb temperature. This adjustment aims to partially decouple the demand and supply sides, resulting in the chillers being operated in high efficiency or turned off, thereby reducing the operational energy consumption of the cooling system. Compared to the traditional rule-based control strategy, the model predictive control strategy reduced the overall energy consumption by 3.13% and operating costs by 3.33%.

Copyright of Chinese Journal of Refrigeration Technology is the property of Shanghai Society of Refrigeration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Refrigeration Technology is the property of Shanghai Society of Refrigeration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To reduce energy consumption in data center cooling systems and ensure the healthy and rapid development of data centers, this study focuses on the cooling system of a data center in Guangzhou. Addressing the efficiency variations of chillers at different Partial Load Rates (PLR), a novel model predictive control method is proposed. The study employs Gaussian process regression to establish a chillers’ operational efficiency model and utilizes Long ShortTerm Memory (LSTM) neural networks to create an outdoor wet bulb temperature time series prediction model. Based on the data center cooling load and the prediction of outdoor wet bulb temperature, the cooling system’s chilled water storage and discharge modes, storage and discharge capacity, the number of chillers, and the PLR are adjusted. This adjustment aims to partially decouple the demand and supply sides, resulting in the chillers being operated in high efficiency or turned off, thereby reducing the operational energy consumption of the cooling system. To evaluate the performance of this strategy, annual energy consumption simulations were conducted, compared to the traditional rule-based control strategy, the model predictive control strategy reduced the overall energy consumption by 3.13% and operating costs by 3.33%. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Refrigeration Technology is the property of Shanghai Society of Refrigeration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Refrigeration Technology is the property of Shanghai Society of Refrigeration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Refrigeration Technology is the property of Shanghai Society of Refrigeration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to explore the applicability and application characteristics of water-side evaporative cooling technology in data center, this paper discusses the thermal environment characteristics of data center and the temperature relationship in the heat transfer process of air conditioning system in computer room, and analyzes the application cases of open cooling tower, closed cooling tower and evaporative cooling chiller in data center. It is concluded that data centers in dry areas with outdoor air conditioning design and wet-bulb temperature lower than 22 °C in summer can completely use water side evaporative cooling to provide cooling for data centers, and the auxiliary mechanical cooling mode in non-dry areas is beneficial to improve the natural cooling time of data centers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)