데이터센터 냉방 에너지 절약을 위한 최적 냉수 유량 예측·제어 알고리즘 개발Development of Optimal Chilled Water Mass Flow Rate Prediction and Control Algorithm for Data Center Cooling Energy Saving
- Authors
- 박보랑; 최영재; 현지연; 태영란; 문진우
- Issue Date
- 2021
- Publisher
- 한국생태환경건축학회
- Keywords
- 데이터센터; 냉방 에너지; 인공지능; 최적 제어알고리즘; Data Center; Cooling Energy; Artificial Intelligent; Optimal Control Algorithm
- Citation
- KIEAE Journal, v.21, no.3, pp 47 - 53
- Pages
- 7
- Journal Title
- KIEAE Journal
- Volume
- 21
- Number
- 3
- Start Page
- 47
- End Page
- 53
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/51074
- DOI
- 10.12813/kieae.2021.21.3.047
- ISSN
- 2288-968X
2288-9698
- Abstract
- Purpose: This study aimed at developing an adaptive control algorithm using ANN based on chilled water mass flow control to provide the optimal indoor thermal environment of the data center and save cooling energy. Method: The predictive model inherent in the control algorithm uses the model developed in preliminary research. The control algorithm including the predictive model was developed using three techniques with relearning function. To verify the adaptability of the finally selected ANN prediction-based adaptive control algorithm, it is compared and evaluated through simulations with the existing widely applied ON-OFF and PID controllers. Result: Among the three relearning techniques, the RMSE of the control algorithm to which the sliding window was applied is 0.04 (℃), which has the highest prediction accuracy. Thus, was selected as the final control algorithm model. To verify the adaptability and scalability of the selected ANN control algorithm, the containment size and the set-temperature scenario were changed into two and compared and analyzed with ON-OFF, PID controller using simulation. As a result, the ANN controller showed the highest accuracy with RMSE 0.23℃ and 0.24℃, respectively, in both control scenarios. Also, It showed the best performance in terms of maximum temperature difference and energy consumption. That is, the adaptability and scalability of the ANN-based control algorithm to the new environment have been reasonably verified.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/51074)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.