滑动窗口协议
人工神经网络
适应性
气流
算法
计算机科学
温度控制
冷负荷
工程类
模拟
控制理论(社会学)
控制工程
人工智能
窗口(计算)
空调
控制(管理)
操作系统
生物
机械工程
生态学
作者
Bo Keun Park,Young Deuk Choi,Eun Ha Choi,Jin Soo Moon
标识
DOI:10.1016/j.jobe.2022.104167
摘要
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. • Development of chilled water flow control algorithm for energy saving of datacenter. • The CRAH supply air temperature prediction model is based on three retraining ANNs. • Creation of adaptive cold water flow control algorithm including a predictive model. • The algorithm shows the highest performance in the sliding window retraining. • The development model is highly adaptable and can be applied to a new environment.
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