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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.
송전제약과 등가운전시간을 고려한 장기 예방정비계획 최적화에 관한 연구
신한솔(Hansol Shin),김형태(Hyoungtae Kim),이성우(Sungwoo Lee),김욱(Wook Kim) 대한전기학회 2017 전기학회논문지 Vol.66 No.2
Most of the existing researches on systemwide optimization of generator maintenance scheduling do not consider the equivalent operating hours(EOHs) mainly due to the difficulties of calculating the EOHs of the CCGTs in the large scale system. In order to estimate the EOHs not only the operating hours but also the number of start-up/shutdown during the planning period should be estimated, which requires the mathematical model to incorporate the economic dispatch model and unit commitment model. The model is inherently modelled as a large scale mixed-integer nonlinear programming problem and the computation time increases exponentially and intractable as the system size grows. To make the problem tractable, this paper proposes an EOH calculation based on demand grouping by K-means clustering algorithm. Network congestion is also considered in order to improve the accuracy of EOH calculation. This proposed method is applied to the actual Korean electricity market and compared to other existing methods.
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.