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- W3095521836 abstract "Purpose: This study aimed at developing a temperature prediction model for a containment data center. The predictive model must be guaranteed with stability and accuracy in order to be used for real-time control. Therefore, statistical evaluation was conducted to verify the prediction performance of the proposed model. Method: The predictive models were developed using four representative machine learning algorithms. A thermodynamic based containment data center and cooling system were modeled by MATLAB & Simulink software. The initial and optimized models were evaluated by R² and Cv(RMSE), and the model with the highest performance was applied to the simulation. Result: In the initial models, RF and ANN presented highest accuracy on R² (0.89) and Cv(RMSE) (17.85%), respectively. After the optimization, ANN presented the best prediction performance on both R² (0.99) and Cv(RMSE) (0.94%). The result supports the accuracy and stability of the ANN model to be used for real-time control, and based on which the optimal control algorithm will be developed on further study." @default.
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- W3095521836 date "2020-10-31" @default.
- W3095521836 modified "2023-10-18" @default.
- W3095521836 title "Development of Supply Air Temperature Prediction Model for Optimal Control Algorithm of Containment Data Center" @default.
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- W3095521836 doi "https://doi.org/10.12813/kieae.2020.20.5.159" @default.
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