컨테인먼트형 데이터센터 최적 제어 알고리즘을 위한 열환경 예측모델 개발Development of Supply Air Temperature Prediction Model for Optimal Control Algorithm of Containment Data Center
- Authors
- 최영재; 박보랑; 조지현; 문진우
- Issue Date
- 2020
- Publisher
- 한국생태환경건축학회
- Keywords
- Data Center; Machine Learning; Optimal Control Algorithm; 데이터센터; 기계학습; 최적 제어알고리즘
- Citation
- KIEAE Journal, v.20, no.5, pp 159 - 164
- Pages
- 6
- Journal Title
- KIEAE Journal
- Volume
- 20
- Number
- 5
- Start Page
- 159
- End Page
- 164
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52585
- DOI
- 10.12813/kieae.2020.20.5.159
- ISSN
- 2288-968X
2288-9698
- 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.
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