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On-Line Model Predictive Control for Energy Efficiency in Data Center
데이터센터 에너지 효율화를 위한 급기팬 온라인 예측제어

한국정밀공학회지 = Journal of the Korean Society for Precision Engineering, v.38 no.12, 2021년, pp.943 - 951  

Chu, Min Sik ,  Kim, Hyun Ah ,  Lee, Kyu Jong ,  Kang, Ji Hoon

초록이 없습니다.

참고문헌 (36)

  1. Cho, J.-K. and Kim, B.-S., “The Cooling and Air Distribution Systems for the Optimal it Environment Control in the (Internet) Data Center,” Journal of the Architectural Institute of Korea Planning & Design, Vol. 24, No. 2, pp. 313-320, 2008. 

  2. Choi, Young Jae, Park, Bo Rang, Choi, Eun Ji, Moon, Jin Woo. Analysis of Air Conditioning Methods for Providing Optimal Thermal Environment in Data Center. KIEAE journal = 한국생태환경건축학회논문집, vol.18, no.6, 97-102.

  3. Durand-Estebe, B., Le Bot, C., Mancos, J.N., Arquis, E.. Data center optimization using PID regulation in CFD simulations. Energy and buildings, vol.66, 154-164.

  4. 10.1109/RTSS.2014.27 

  5. Fang, Qiu, Wang, Jun, Zhu, Han, Gong, Qi. Using Model Predictive Control in Data Centers for Dynamic Server Provisioning. IFAC proceedings volumes, vol.47, no.3, 9635-9642.

  6. Lee, J.-W., Yoon, J.-H., Yang, I.-H., Lee, K., and Oh, J.-E., “PID Vibration Control of Precision Production Facilities,” Proc. of the Korean Society of Precision Engineering Conference, pp. 227-228, 2009. 

  7. Unar, M. A., Murray-Smith, D., and Shah, S. A., “Design and Tuning of Fixed Structure PID Controllers-A Survey,” Ph.D. Thesis, University of Glasgow, 1995. 

  8. Seo, J.-W., Kim, K.-H., Kim, S.-J., Oh, M., and Lee, T.-H., “Control of Atmospheric Distillation Unit Using Model Predictive Control Technique,” Korean Chemical Engineering Research, Vol. 40, No. 2, pp. 152-158, 2002. 

  9. Lee, Taekgyu, Kang, Yeonsik. Development of Deep Artificial Neural Network Controller Based on Non-linear Model Predictive Control Data for Real-time Autonomous Driving. 제어·로봇·시스템학회 논문지 = Journal of institute of control, robotics and systems, vol.26, no.5, 302-311.

  10. Li, Yuanlong, Wen, Yonggang, Tao, Dacheng, Guan, Kyle. Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning. IEEE transactions on cybernetics, vol.50, no.5, 2002-2013.

  11. Yang, In-Ho. Development of an Artificial Neural Network Model to Predict the Optimal Pre-cooling Time in Office Buildings. Journal of Asian architecture and building engineering, vol.9, no.2, 539-546.

  12. Lim, Heonyoung, Kang, Yeonsik, Kim, Changhwan, Kim, Jongwon. Experimental Verification of Nonlinear Model Predictive Tracking Control for Six-Wheeled Unmanned Ground Vehicles. International journal of precision engineering and manufacturing, vol.15, no.5, 831-840.

  13. Yudong Ma, Borrelli, F., Hencey, B., Coffey, B., Bengea, S., Haves, P.. Model Predictive Control for the Operation of Building Cooling Systems. IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society, vol.20, no.3, 796-803.

  14. Bellman, Richard. Dynamic Programming. Science, vol.153, no.3731, 34-37.

  15. Ljung, L., “System Identification: Theory for the User,” Prentice Hall, 2nd Ed., 1999. 

  16. Richalet, J., Rault, A., Testud, J.L., Papon, J.. Model predictive heuristic control - Applications to industrial processes. Automatica : the journal of IFAC, the International Federation of Automatic Control, vol.14, no.5, 413-428.

  17. Bitmead, R. R., Gevers, M., and Wertz, V., “Adaptive Optimal Control the Thinking Man’s GPC,” Prentice Hall, 1990. 

  18. Bitmead, R. R., Gevers, M., and Wertz, V., “Adaptive Optimal Control the Thinking Man’s GPC,” Prentice Hall, 1990. 

  19. 10.1007/978-1-4612-5612-0_14 Ljung, L. and Söderström, T., “Theory and Practice of Recursive Identification,” MIT Press, 1983. 

  20. 10.4310/CIS.2006.v6.n4.a3 

  21. Abbasi-Yadkori, Y. and Szepesvári, C., “Regret Bounds for the Adaptive Control of Linear Quadratic Systems,” Proc. of the 24th Annual Conference on Learning Theory, pp. 1-26, 2011. 

  22. Lee, Jai Hak, Kim, Kang Su. Optimal Design of Boom for Telescopic Boom Type Forklift Truck. 한국정밀공학회지 = Journal of the Korean Society of Precision Engineering, vol.37, no.6, 457-464.

  23. Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M., “Response Surface Methodology: Process and Product Optimization Using Designed Experiments,” John Wiley & Sons, 3rd Ed., 2009. 

  24. Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M., “Time Series Analysis: Forecasting and Control,” John Wiley & Sons, 5th Ed., 2015. 

  25. Kelman, Anthony, Borrelli, Francesco. Bilinear Model Predictive Control of a HVAC System Using Sequential Quadratic Programming. IFAC proceedings volumes, vol.44, no.1, 9869-9874.

  26. Abbasi-Yadkori, Y. and Szepesvári, C., “Bayesian Optimal Control of Smoothly Parameterized Systems,” Proc. of the 31st Conference on Uncertainty in Artificial Intelligence, pp. 2-11, 2015. 

  27. Abeille, M. and Lazaric, A., “Thompson Sampling for Linear-Quadratic Control Problems,” Proc. of the 20th International Conference on Artificial Intelligence and Statistics, pp. 1246-1254, 2017. 

  28. 10.1109/ALLERTON.2017.8262873 

  29. Lazic, N., Boutilier, C., Lu, T., Wong, E., Roy, B., et al., “Data Center Cooling Using Model-Predictive Control,” Proc. of the 22nd Conference on Neural Information Processing Systems, pp. 3814-3823, 2018. 

  30. Ibrahimi, M., Javanmard, A., and Van Roy, B., “Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems,” Proc. of the Conference on Neural Information Processing Systems, pp. 2645-2653, 2012. 

  31. Gao, J., “Machine Learning Applications for Data Center Optimization,” https://storage.googleapis.com/pub-tools-public-publication-data/pdf/42542.pdf (Accessed 19 NOVEMBER 2021) 

  32. Evans, R. and Gao, J., “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40 (Accessed 19 NOVEMBER 2021) 

  33. Yudong Ma, Kelman, Anthony, Daly, Allan, Borrelli, Francesco. Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Simulation, and Experiments. IEEE control systems magazine, vol.32, no.1, 44-64.

  34. ASHRAE, “Thermal guidelines for Data Processing Environments,” https://airatwork.com/wp-content/uploads/ASHRAETC99.pdf (Accessed 19 NOVEMBER 2021) 

  35. 10.1007/978-0-387-84858-7 

  36. Samsung SDS, “Brightics Machine Learning,” https://www.samsungsds.com/kr/ai-ml/brightics-machine-learning.html (Accessed 19 NOVEMBER 2021) 

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