Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center
A multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facili...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/8/1474 |
| _version_ | 1817989752198004736 |
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| author | Leehter Yao Jin-Hao Huang |
| author_facet | Leehter Yao Jin-Hao Huang |
| author_sort | Leehter Yao |
| collection | DOAJ |
| description | A multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facilities. However, the rack intake air temperature tends to increase if the energy saving control scheme applied to AHU and chillers is conducted inappropriately. Both ACS energy consumption and rack intake air temperature stabilization are set as two objectives for multi-objective optimization. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimization problem. In order for the NSGA-II to evaluate fitness functions that are both the ACS total power consumption and AHU outlet cold air temperature deviations from a specified range, neural network models are utilized. Feedforward neural networks are utilized to learn the power consumption models for both chillers and AHUs as well as the AHU outlet cold air temperature based on the recorded data collected in the field. The effectiveness and efficiency of the proposed energy saving control scheme is verified through practical experiments conducted on a campus data center ACS. |
| first_indexed | 2024-04-14T00:51:02Z |
| format | Article |
| id | doaj.art-b05a96fa0f7641fa9a52c516691b707d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T00:51:02Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b05a96fa0f7641fa9a52c516691b707d2022-12-22T02:21:48ZengMDPI AGEnergies1996-10732019-04-01128147410.3390/en12081474en12081474Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data CenterLeehter Yao0Jin-Hao Huang1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, TaiwanA multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facilities. However, the rack intake air temperature tends to increase if the energy saving control scheme applied to AHU and chillers is conducted inappropriately. Both ACS energy consumption and rack intake air temperature stabilization are set as two objectives for multi-objective optimization. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimization problem. In order for the NSGA-II to evaluate fitness functions that are both the ACS total power consumption and AHU outlet cold air temperature deviations from a specified range, neural network models are utilized. Feedforward neural networks are utilized to learn the power consumption models for both chillers and AHUs as well as the AHU outlet cold air temperature based on the recorded data collected in the field. The effectiveness and efficiency of the proposed energy saving control scheme is verified through practical experiments conducted on a campus data center ACS.https://www.mdpi.com/1996-1073/12/8/1474data centerchillerair handling unitmulti-objective optimizationpower usage effectiveness (PUE), rack cooling index (RCI) |
| spellingShingle | Leehter Yao Jin-Hao Huang Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center Energies data center chiller air handling unit multi-objective optimization power usage effectiveness (PUE), rack cooling index (RCI) |
| title | Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center |
| title_full | Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center |
| title_fullStr | Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center |
| title_full_unstemmed | Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center |
| title_short | Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center |
| title_sort | multi objective optimization of energy saving control for air conditioning system in data center |
| topic | data center chiller air handling unit multi-objective optimization power usage effectiveness (PUE), rack cooling index (RCI) |
| url | https://www.mdpi.com/1996-1073/12/8/1474 |
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