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BROWSE대한건축학회 논문집(구조계)2017-11 > DETAIL
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Title   빙축열 시스템의 익일 방냉량 예측 기계학습 모델 및 제어 / Machine Learning Models for Prediction and Control of an Ice Thermal Storage System in an Existing Building
Authors   신한솔(Shin, Han-Sol) ; 서원준(Suh, Won-Jun) ; 추한경(Chu, Han-Gyeong) ; 라선중(Ra, Seon-Jung) ; 박철수(Park, Cheol-Soo)
Organization   대한건축학회
Source   대한건축학회논문집 구조계, Vol.33 No.11(2017-11)
Page   Start Page(63) Total Page(8)
ISSN   1226-9107
Classification   구조  
Keywords   빙축열 시스템 ; 기계학습 ; 모델 예측 제어 ; 사무소 건물 ; 시뮬레이션 모델//ice thermal storage system ; machine learning ; model predictive control ; office building ; simulation model
Abstract2   In South Korea, an ice thermal storage system is popular because night-time electricity rate is cheaper than daytime rate. A spherical ice ball system is one of the most popular ice thermal storage systems used in Korea. However, it is difficult to estimate the degree of freezing and defrosting of the spherical ice ball system and thus, excessive icing commonly occurs in order to prevent any shortage of stored ice. If this rule-of thumb control can be replaced by a simulation model-based control, there would be significant potential for energy savings. In this study, the authors developed 25 machine learning simulation models for the spherical ice thermal storage system installed in a 30-story office building (gross floor area: 32,600m2) located in Seoul, Korea. Five different machine learning algorithms (Artificial Neural Network, Support Vector Machine, Gaussian Process, Random Forest, and Genetic Programming) were used for five different input scenarios, respectively. The 25 machine learning models are accurate enough to predict the amount of icing required for the following daytime. In addition, with the use of Model Predictive Control (MPC), 16.8% of excessive icing during overnight can be reduced and 15% of cooling energy (chiller, cooling tower, Brine pump, etc.) can be saved.
Location   대한건축학회