We developed control algorithms based on one of three artificial-intelligence-based retraining techniques (sliding window, vector adaptation, and vector augmentation) to provide the optimal indoor temperature and save on the energy expenditure for cooling in data centers. The artificial neural network prediction model predicts the computer room air handler supply air temperature of a central chilled water system and is added to the control algorithm. The proposed algorithm can determine the optimal chilled water flow rate required to cool the server to the set temperature by using the predicted computer room air handler supply air temperature. We developed a control algorithm embedded in an artificial neural network predictive model that includes three retraining techniques. Afterward, we compared the control performance and verified its adaptability by using computer simulation. When using the algorithm with sliding window control, the root mean-squared error between the set temperature and the control temperature was 0.08 °C, the maximum error was 0.81 °C, and the cooling load was 21,026.27 kWh. The accuracy, stability, and energy-saving ability of the sliding window control algorithm were higher those of the other two algorithms, and its superior adaptability and scalability under changing environmental conditions were demonstrated. 

Adaptive control algorithm with a retraining technique to predict the optimal amount of chilled water in a data center cooling system

Purpose: As cooling energy accounts for 50.5% of the data center total energy use, it is necessary to reduce cooling energy with optimal control method. Thus, this study aimed at developing an adaptive control algorithm for data center thermal environment based on Artificial Neural Network (ANN). Method: A data center thermal environment model was built to obtain variable data which is used to train and evaluate performance of the prediction model and algorithm. The thermal environment prediction model was developed using ANN and optimization process was conducted by the Bayesian Optimization Algorithm. The adaptive control algorithm, which embedded the prediction model, adopted the Sliding Windows method and was optimized to maximize the control performance. Result: The performance evaluation of the developed algorithm was conducted compared with non-adaptive algorithm. As a result, the adaptive algorithm presented better performance than the non-adaptive with 0.45 of RMSE. Therefore, the developed algorithm secured the stability and accuracy and will be applied to supervisory control platforms.

Development of a Prediction Model and an Adaptive Control Algorithm for the Data Center Thermal Environment

A Study on the Humidity Paper for Indicating Moisture Absorbed in R-22

/pdf/energy-saving-effect-of-cchp-system-using-high-temperature-33pqmbuw.pdf

Energy Saving Effect of CCHP System Using High Temperature Polymer Electrolyte Fuel Cell for Data Centers

Recently, because of the deterioration of the nuclear generating station and increase of the cooler and heater, energy problem is increasing. To save the energy, the free cooling system is developed. The free cooling system is that cool the water to use cooled air in winter and is used in industrial process or data center. Yoon check the energy of free cooling system with dry cooler in korea. In this study, the value of the free cooling system with dry cooler is confirmed through using the NPV that is economic evaluation. when temperature degree of the cooled water is 10, in Chuncheon and Seoul the value is the most high. When temperature degree of the cooled water is 20, in Ulsan the value is the most high. As the result, because the using the temperature degree of the cooled water is high in the industrial process, the free cooling system is advantageous in korea.

A Study on Application of the Free Cooling System with Dry Cooler Using Economic Evaluation

Recently, Free cooling system usage has increased at many buildings in intermediate and winter season. Free cooling system is used to reduce the energy consumption of refrigeration in that season. Free cooling system is refrigeration system using cooled water. In general, this system is applied with the building having refrigeration load at all time such as a data center. In this study, energy consumption of a data center taking free cooling system in Ulsan was evaluated by the software HYSYS. the main result is as in the following : free cooling system is effective from January to April and from November to December. In case of Ulsna in 2013, using free coolng system is able to spend refrigeration energy of about 15% less than existing system. According to this result, it is appropriated that free cooling system is used in building having refrigeration load at all time such as data center.

/pdf/deiteo-senteoyi-oeginaengsunaengbang-siseuteme-daehan-eneoji-rynx73ldw5.pdf

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데이터 센터의 외기냉수냉방 시스템에 대한 에너지 절감효과 분석

전력절감형 외기 냉방시스템