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랜덤 포레스트와 데이터 전처리를 이용한 냉동기 기계학습 모델 개발
신한솔(Shin, Han-Sol),박철수(Park, Cheol-Soo) 대한건축학회 2017 大韓建築學會論文集 : 構造系 Vol.33 No.9
It has been widely acknowledged that a machine learning model can be used as a surrogate to a first-principle based dynamic simulation model. The accuracy and computation efficiency of a machine learning model is dependent on a combination of input variables. The random forest algorithm, one of the machine learning algorithms, can calculate a variable importance that determines the influence of each input variable on the output of the model. In this study, the authors developed three random forest models of a chiller in an existing building as follows: (1) Model A consisting of 12 measured variables from BEMS data, (2) Model B consisting of 2 measured input variables plus 4 new variables constructed by random selection, and (3) Model C consisting of 4 measured input variables plus 2 new variables constructed based on a physics-based equation. The CVRMSE of the three models are 8.56%, 5.44%, and 4.28%, respectively. The findings of this study can be summarized threefold: (1) all three random forest models are good enough to describe the dynamics of the chiller system, (2) the random forest machine learning algorithm can be used to develop a simulation model of the system, and (3) an accurate model can be constructed either by the random selection or the physics-based equation, even when a few input variables are given.
빙축열 시스템의 익일 방냉량 예측 기계학습 모델 및 제어
신한솔(Shin, Han-Sol),서원준(Suh, Won-Jun),추한경(Chu, Han-Gyeong),라선중(Ra, Seon-Jung),박철수(Park, Cheol-Soo) 대한건축학회 2017 大韓建築學會論文集 : 構造系 Vol.33 No.11
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.
점진적 샘플링과 정규 상호정보량을 이용한 온라인 기계학습 공조기 급기온도 예측 모델 개발
추한경(Chu, Han-Gyeong),신한솔(Shin, Han-Sol),안기언(Ahn, Ki-Uhn),라선중(Ra, Seon-Jung),박철수(Park, Cheol Soo) 대한건축학회 2018 大韓建築學會論文集 : 構造系 Vol.34 No.6
The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction (5.4% → 1.3%).
공순구,박지훈,신한솔,Kong, Soon-Ku,Park, Ji-Hun,Shin, Han-Sol 한국비블리아학회 2012 한국비블리아학회지 Vol.23 No.4
본 연구는 일본에서의 지역사회에 개방하는 학교도서관에 대한 개방과 배치유형 및 공간구성에 관한 연구로서 학교내 설치되어 있는 학교도서관을 여러 가지 대안으로 개방함으로써 지역주민의 도서관 이용접근성을 증진시키고 나아가 시민의 정보접근환경을 개선시키는 방안을 모색하고자 한다. 일본의 경우 1953년에 학교도서관법의 단일법령을 세계 최초로 제정하였을 정도로 학교교육에서 차지하는 도서관의 중요성을 공감하고 있었으며, 이러한 움직임에 힘입어 학교도서관의 설치율이 100%에 이르는 성과를 이뤄냈다. 또한 이런 도서관이 지역에 개방되어 지역주민의 도서관 이용을 증진시키는 과정이 이루어지고 있으며, 전체 소 중 고교 중 11.6%에 달하는 학교도서관이 주민에게 개방되었다. 점차 노령화되고 초등학교 시설내의 이용이 저감되는 시점에서 학교교사의 활용방안을 도서관의 확장과 개방화를 통해 이용대안을 모색하고 학교와 지역마다의 특성을 다양하게 반영할 수 있는 여러 대안이 제시된다면 학교도서관 개방이 보다 적극적으로 이루어질 것으로 기대하며, 이는 정보화시대에 국민의 평등한 정보접근환경제공과 평생교육의 제공으로 이루어질 것이다. 학교도서관이 지역사회와 적합한 규모와 형태로 설치되기 위한 유형과 관련된 연구를 통하여 학교도서관이 개방되어 본연의 기능을 수행하는 데에 기여할 것으로 기대한다. This is a study on types and current status of school library in japan that are open to public. it aims to increase library access and further improvement of accessible information environment by opening the libraries within schools. In case of Japan, libraries' importance to school education enacted world's first School library act by 1953 and installation rate of school libraries is achieved up to 100%. These libraries opened to its local community is in the process of increasing library usage and now in total of 11.6% of lower, middle, and upper school are already opened to their local community. Considering decreasing usage of facilities in schools, teacher vacant time, expanding libraries, and other various alternatives should be considered together. It will lead to equal access to information environment and lifelong education. The study suggests ways of installing school libraries in appropriate sizes and forms within their local societies. It also propose possible ways to open school libraries to perform their own functions.