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Hiroshi Endo,Shigeto Suzuki,Hiroyoshi Kodama,Takeshi Hatanaka,Hiroyuki Fukuda,Masayuki Fujita 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
In this research, we propose an energy-efficient predictive control system with outside-air-introduced-type air conditioner in large-scale data center. The system consists of two parts; a predictive part and a control part. We propose to use a dynamic model construction technique called just-in-time (JIT) modeling in the predictive part. It can estimate the air temperature and humidity in the DC room and temperature at the inlet of servers at a high accuracy while the update of data center model is constructed naturally. We also propose to use a control algorithm that utilizes air enthalpy at outside and DC room in addition to the air temperature and humidity in the control part. Owing to the enthalpy, we achieve an effective energy-saving with both wet and dry air. We introduced this predictive control system to a large-scale data center and had verified the improvement in the energy-saving performance for few month. The air temperature and humidity after one hour could be successfully estimated with an accuracy of correlation coefficient of 0.97 or more using the proposed JIT modeling. Furthermore, we demonstrated that the power consumption of the DC room was significantly reduced by 28.9% using the proposed control algorithm compared to that of the conventional control algorithm.
Masatoshi Ogawa,Hiroshi Endo,Hiroyuki Fukuda,Hiroyoshi Kodama,Toshio Sugimoto,Takeshi Horie,Tsugito Maruyama,Masao Kondo 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
A cooling control method based on a model predictive control (MPC) for a modular datacenter utilizing the fresh-air is proposed. The proposed method reduces the total energy consumption of information technology (IT) equipment and cooling facilities in the data center, while considering a relationship between energy-savings and the temperature information of IT equipment. This method based on MPC controls the central processing unit (CPU) temperature in servers by facility fans for cooling. To design the proposed method, it is developed a prediction model that represents the CPU temperature by the revolution speed of facility fans, the fresh-air temperature, utilization of servers, and other factors. Furthermore, the proposed control method is applied to the actual modular data center. The energy consumption of the proposed method is compared with that of a traditional method, which has controlled the temperature difference between the inlet and outlet of the server racks based on proportional integral (PI) control. Actual comparison experiments with traditional method are provided to validate effectiveness of the proposed method. The results show that the proposed method realizes energy-savings of more than 20% compared to the traditional control method in the actual modular datacenter.