Abstract
A multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facilities. However, the rack intake air temperature tends to increase if the energy saving control scheme applied to AHU and chillers is conducted inappropriately. Both ACS energy consumption and rack intake air temperature stabilization are set as two objectives for multi-objective optimization. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimization problem. In order for the NSGA-II to evaluate fitness functions that are both the ACS total power consumption and AHU outlet cold air temperature deviations from a specified range, neural network models are utilized. Feedforward neural networks are utilized to learn the power consumption models for both chillers and AHUs as well as the AHU outlet cold air temperature based on the recorded data collected in the field. The effectiveness and efficiency of the proposed energy saving control scheme is verified through practical experiments conducted on a campus data center ACS.
Highlights
Following the increasing demand for Internet of Things (IOT), big data, cloud computing, artificial intelligence, etc., more information technology (IT) equipment such as servers, data storage, and network communication devices as well as uninterruptible power systems have been placed into data centers
For the purpose of illustration, the power consumptions of chiller and air handling units (AHU) as well as the AHU outlet cold air temperature estimated by those three feedforward neural network (FNN) models for a typical testing day are compared with real data in Figure 3a,b, Figure 4a,b and Figure 5a,b, respectively
AHUs while keeping AHU outlet cold air temperature within a specified range. Both commonly used standardized metrics for data centers including power usage effectiveness (PUE) and rack cooling index (RCI) were utilized as the objects for optimization
Summary
Following the increasing demand for Internet of Things (IOT), big data, cloud computing, artificial intelligence, etc., more information technology (IT) equipment such as servers, data storage, and network communication devices as well as uninterruptible power systems have been placed into data centers. For practical control of ACS in data center, variants for minimizing PUE and maximizing RCI are developed for multi-objective optimization It will be shown in the paper that the minimization of PUE is achieved by power consumption minimization of chillers and AHUs. it is impractical to monitor and control intake temperature of every rack inside cabinets. A multi-objective optimization approach that simultaneously optimizes power consumption of chillers and AHUs as well as AHU outlet cold air temperature for the ACS in data center is introduced. The proposed NSGA-II based multi-objective optimization approach has been practically applied to the ACS of a campus data center and the average energy saving ratios are 23.4% and 19.6% for a typical winter and summer day, respectively.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
