Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage
Addressing the critical need for enhanced power usage effectiveness in data centers (DCs), this study pioneers an improved convolutional long short-term memory with deep neural network (CLDNN) model, enriched with attention mechanisms for precise DC performance prediction. We rigorously evaluate our...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Thermofluids |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666202724003070 |
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| author | Yang-Cheng Shih Sathesh Tamilarasan Chin-Sheng Chen Omid Ali Zargar Yean-Der Kuan |
| author_facet | Yang-Cheng Shih Sathesh Tamilarasan Chin-Sheng Chen Omid Ali Zargar Yean-Der Kuan |
| author_sort | Yang-Cheng Shih |
| collection | DOAJ |
| description | Addressing the critical need for enhanced power usage effectiveness in data centers (DCs), this study pioneers an improved convolutional long short-term memory with deep neural network (CLDNN) model, enriched with attention mechanisms for precise DC performance prediction. We rigorously evaluate our model against leading architectures – long short-term memory (LSTM), attention-based (att-LSTM), convolutional LSTM (CNN-LSTM), gated recurrent unit (GRU), and CNN-GRU – to affirm its superiority in predictive accuracy and robustness. The integration of convolutional layers processes hourly data inputs efficiently, reducing complexity and improving pattern detection. A subsequent flattening layer optimizes accuracy, while a dual-layered LSTM and a deep neural network delve into frequency, temporal dynamics, and complex data relationships. Incorporating an attention mechanism into the att-CLDNN model has revolutionized predictive analytics in DC energy management, significantly enhancing accuracy by highlighting crucial data interdependencies. This model's unparalleled precision, evidenced by achieving the lowest Mean Squared Error (MSE) of 0.000179, the minimum Mean Absolute Error (MAE) of 0.01048, and the highest R2 Score of 0.977031, underscores its effectiveness. Crucially, this breakthrough fosters sustainability in energy management, promoting greener DC operations through precise energy use predictions, leading to substantial energy savings and reduced carbon emissions, in alignment with global sustainability objectives. |
| format | Article |
| id | doaj-art-769bbfd7e1a44168bae679af65ada56a |
| institution | Kabale University |
| issn | 2666-2027 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Thermofluids |
| spelling | doaj-art-769bbfd7e1a44168bae679af65ada56a2024-12-13T11:04:07ZengElsevierInternational Journal of Thermofluids2666-20272024-11-0124100866Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usageYang-Cheng Shih0Sathesh Tamilarasan1Chin-Sheng Chen2Omid Ali Zargar3Yean-Der Kuan4Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei, 10608, Taiwan; Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei, 10608, TaiwanDepartment of Refrigeration, Air Conditioning and Energy Engineering, National Chin-Yi University of Technology, 57, Sec.2, Zhongshan Rd, Taiping Dist., Taichung City, 411030, Taiwan; Corresponding author at: Department of Refrigeration, Air Conditioning and Energy Engineering, National Chin-Yi University of Technology, No.57, Sec.2, Zhongshan Rd., Taiping Dist., Taichung, 411030, Taiwan.Graduate Institute of Automation Technology, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei, 10608, TaiwanDepartment of Mechanical Engineering, National Chin-Yi University of Technology, 57, Sec.2, Zhongshan Rd, Taiping Dist., Taichung City, 411030, TaiwanDepartment of Refrigeration, Air Conditioning and Energy Engineering, National Chin-Yi University of Technology, 57, Sec.2, Zhongshan Rd, Taiping Dist., Taichung City, 411030, TaiwanAddressing the critical need for enhanced power usage effectiveness in data centers (DCs), this study pioneers an improved convolutional long short-term memory with deep neural network (CLDNN) model, enriched with attention mechanisms for precise DC performance prediction. We rigorously evaluate our model against leading architectures – long short-term memory (LSTM), attention-based (att-LSTM), convolutional LSTM (CNN-LSTM), gated recurrent unit (GRU), and CNN-GRU – to affirm its superiority in predictive accuracy and robustness. The integration of convolutional layers processes hourly data inputs efficiently, reducing complexity and improving pattern detection. A subsequent flattening layer optimizes accuracy, while a dual-layered LSTM and a deep neural network delve into frequency, temporal dynamics, and complex data relationships. Incorporating an attention mechanism into the att-CLDNN model has revolutionized predictive analytics in DC energy management, significantly enhancing accuracy by highlighting crucial data interdependencies. This model's unparalleled precision, evidenced by achieving the lowest Mean Squared Error (MSE) of 0.000179, the minimum Mean Absolute Error (MAE) of 0.01048, and the highest R2 Score of 0.977031, underscores its effectiveness. Crucially, this breakthrough fosters sustainability in energy management, promoting greener DC operations through precise energy use predictions, leading to substantial energy savings and reduced carbon emissions, in alignment with global sustainability objectives.http://www.sciencedirect.com/science/article/pii/S2666202724003070Data centerPower usage effectivenessConvolutional long short-term memoryDeep neural networkIntegrated modelAttention mechanism |
| spellingShingle | Yang-Cheng Shih Sathesh Tamilarasan Chin-Sheng Chen Omid Ali Zargar Yean-Der Kuan Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage International Journal of Thermofluids Data center Power usage effectiveness Convolutional long short-term memory Deep neural network Integrated model Attention mechanism |
| title | Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage |
| title_full | Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage |
| title_fullStr | Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage |
| title_full_unstemmed | Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage |
| title_short | Attention-based integrated deep neural network architecture for predicting the effectiveness of data center power usage |
| title_sort | attention based integrated deep neural network architecture for predicting the effectiveness of data center power usage |
| topic | Data center Power usage effectiveness Convolutional long short-term memory Deep neural network Integrated model Attention mechanism |
| url | http://www.sciencedirect.com/science/article/pii/S2666202724003070 |
| work_keys_str_mv | AT yangchengshih attentionbasedintegrateddeepneuralnetworkarchitectureforpredictingtheeffectivenessofdatacenterpowerusage AT satheshtamilarasan attentionbasedintegrateddeepneuralnetworkarchitectureforpredictingtheeffectivenessofdatacenterpowerusage AT chinshengchen attentionbasedintegrateddeepneuralnetworkarchitectureforpredictingtheeffectivenessofdatacenterpowerusage AT omidalizargar attentionbasedintegrateddeepneuralnetworkarchitectureforpredictingtheeffectivenessofdatacenterpowerusage AT yeanderkuan attentionbasedintegrateddeepneuralnetworkarchitectureforpredictingtheeffectivenessofdatacenterpowerusage |