Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers
Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Cleaner Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666790825001375 |
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| author | Shuaiyin Ma Yichun Cao Yang Liu |
| author_facet | Shuaiyin Ma Yichun Cao Yang Liu |
| author_sort | Shuaiyin Ma |
| collection | DOAJ |
| description | Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. This architecture combines time-series generative adversarial network (TimeGAN) for data augmentation with the neural basis expansion analysis for time series (N-BEATS) for prediction, providing a robust solution for accurate energy consumption prediction. TimeGAN enhances the training dataset by generating high-quality synthetic time-series data, effectively mitigating issues of data sparsity and imbalance. N-BEATS, with its modular architecture and strong adaptability to temporal data, ensures precise predictions by capturing both global trends and local variations in energy usage. Experimental results demonstrate the effectiveness of the proposed architecture, exhibiting superior performance across key metrics when compared to traditional models. Specifically, the RMSE of TimeGAN-N-BEATS reduced by more than 8 %, the MSE decreased by over 18 %, and the R2 reached 97.31 %, outperforming baseline models such as long short-term memory and gated recurrent unit, and their attention-enhanced variants. This study highlights the potential of integrating generative and predictive models to optimize energy efficiency in liquid cooling systems, offering valuable insights for sustainable supercomputing operations. |
| format | Article |
| id | doaj-art-d423c2c7109846a5b3a82c0776a863fa |
| institution | DOAJ |
| issn | 2666-7908 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Engineering and Technology |
| spelling | doaj-art-d423c2c7109846a5b3a82c0776a863fa2025-08-20T03:10:47ZengElsevierCleaner Engineering and Technology2666-79082025-07-012710101410.1016/j.clet.2025.101014Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centersShuaiyin Ma0Yichun Cao1Yang Liu2School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an, 710121, China; Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China; Corresponding author. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China.School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, ChinaDepartment of Management and Engineering, Linköping University, SE-581 83 Linköping, Sweden; Industrial Engineering and Management, University of Oulu, 90570 Oulu, Finland; Corresponding author. Department of Management and Engineering, Linköping University, SE-581 83 Linköping, Sweden.Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. This architecture combines time-series generative adversarial network (TimeGAN) for data augmentation with the neural basis expansion analysis for time series (N-BEATS) for prediction, providing a robust solution for accurate energy consumption prediction. TimeGAN enhances the training dataset by generating high-quality synthetic time-series data, effectively mitigating issues of data sparsity and imbalance. N-BEATS, with its modular architecture and strong adaptability to temporal data, ensures precise predictions by capturing both global trends and local variations in energy usage. Experimental results demonstrate the effectiveness of the proposed architecture, exhibiting superior performance across key metrics when compared to traditional models. Specifically, the RMSE of TimeGAN-N-BEATS reduced by more than 8 %, the MSE decreased by over 18 %, and the R2 reached 97.31 %, outperforming baseline models such as long short-term memory and gated recurrent unit, and their attention-enhanced variants. This study highlights the potential of integrating generative and predictive models to optimize energy efficiency in liquid cooling systems, offering valuable insights for sustainable supercomputing operations.http://www.sciencedirect.com/science/article/pii/S2666790825001375Edge computingLiquid cooling systemN-BEATSSupercomputing centersTimeGAN |
| spellingShingle | Shuaiyin Ma Yichun Cao Yang Liu Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers Cleaner Engineering and Technology Edge computing Liquid cooling system N-BEATS Supercomputing centers TimeGAN |
| title | Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers |
| title_full | Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers |
| title_fullStr | Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers |
| title_full_unstemmed | Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers |
| title_short | Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers |
| title_sort | edge computing enabled energy efficiency prediction of immersion cooling system for supercomputing centers |
| topic | Edge computing Liquid cooling system N-BEATS Supercomputing centers TimeGAN |
| url | http://www.sciencedirect.com/science/article/pii/S2666790825001375 |
| work_keys_str_mv | AT shuaiyinma edgecomputingenabledenergyefficiencypredictionofimmersioncoolingsystemforsupercomputingcenters AT yichuncao edgecomputingenabledenergyefficiencypredictionofimmersioncoolingsystemforsupercomputingcenters AT yangliu edgecomputingenabledenergyefficiencypredictionofimmersioncoolingsystemforsupercomputingcenters |