Correlating Supply Demand of Cooling Energy between Gas District Cooling Model with Data Center

Omar, N.S.S. and Jung, L.T. and Rahim, L.A. (2021) Correlating Supply Demand of Cooling Energy between Gas District Cooling Model with Data Center. In: UNSPECIFIED.

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Abstract

This study is to determine the correlation amongst chilled water temperature supply from Gas District Cooling (GDC) operations and demand for cooling and energy demand from Data Centres (DC) operations. At first, the GDC-DC modelling was proposed by Hitachi Research Team in UTP. This is because, UTP has and advantage of GDC to house the campus region with electrical energy and chilled water for air conditioners in UTP's academic buildings, chancellor complex, and UTP mosque. This paper aims to find contribution of real-time system in optimizing the cloud DC that can impact the cooling demand energy demand. The studies on the demand of cooling and energy from the operation of DC has been tested on Linux real-time operating systems with AMD FX850 processors with selected job scheduling algorithms. Pearson's r correlation analysis between GDC DC has shown that there is a significant disparity between the chilled water temperature supply from GDC with cooling demand from DC where r=0.130 which is more than 0.05. Apart from that, Round Robin (RR) algorithm has reduced power consumption in DC but not reducing the cooling demand, while First In First Out (FIFO) algorithm has reduced the cooling demand in DC and the trend is followed by power consumption. © 2021 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 0; Conference of 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021 ; Conference Date: 1 December 2021 Through 2 December 2021; Conference Code:176965
Uncontrolled Keywords: Air conditioning; Computer operating systems; Cooling systems; District heating; Electric power utilization; Energy management; Interactive computer systems; Real time systems; Scheduling algorithms; Temperature, Chilled water; Cooling data; Data center; Datacenter; District cooling; Energy demands; Gas district cooling; Job scheduling algorithms; Supply-demand; Water temperatures, Cooling
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:29
Last Modified: 10 Nov 2023 03:29
URI: https://khub.utp.edu.my/scholars/id/eprint/15364

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