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녹색건축 인증제도의 지속가능성 제고를 위한 평가항목 분석
염동우(Yeom, Dongwoo),이규인(Lee, Kyu-In) 대한건축학회 2016 대한건축학회논문집 Vol.32 No.9
The purpose of this study was to suggest new assessment indicators of G-SEED for sustainability improvement. To do so, current G-SEED was analyzed in sustainable perspective, and compared with the major international certification systems. Preliminary assessment indicators were selected to improve sustainability, and 43 new assessment indicators in three phases were suggested through the specialist survey and the result analysis. The indicators are 25 in phase Ⅰ, 10 in phase Ⅱ and 8 in Phase Ⅲ. Achieved indicators need a further review before acceptance to the current system, and especially, the indicators in phase I were marked highly important by the specialist survey for improving sustainability of the G-SEED. Therefore, additional research and further studies on the acceptance are required in the near future.
염동우(Yeom, Dongwoo),이규인(Lee, Kyu-In) 대한건축학회 2015 대한건축학회논문집 Vol.31 No.3
The purpose of this study was to suggest improvement directions of G-SEED management system. To do this, the international and national green building certification systems were compared and analysed. The Korean green building certification system showed many differences compared with the international systems. The different issues were interviewed and surveyed to the certification specialist group, and the improvement directions were deduced. Most specialists agreed on the necessity of the improvement for management system in general. 12 items required improvement of the present system or adapt new system. Only one item was required to remain as present condition, and the other items needed further studies.
스마트오피스에서 실내 조명환경에 따른 재실자 생체신호 특성 분석 및 업무생산성 예측모델 개발
김태근,염동우,임세헌,윤성국 한국통신학회 2022 韓國通信學會論文誌 Vol.47 No.11
Recently, the need to make a personalized environment using personal bio-signal data from various smart devices has been increasing. To acheive optimal productivity and energy-saving for indoor environments, we conducted a human experiment and analyzed their bio-signal. Based on the results, we proposed machine learning based productivity prediction model. We experimented at Arizona State University to obtain bio-signal at each indoor lighting condition. Among bio-signals, the pupil size and heart rate are selected. We show that pupil size and heart rate highly correlate with indoor lighting environments. Also, pupil size and heart rate correlate with productivity. Based on these results, we propose a decision tree-based productivity prediction model using pupil size and heart rate. The proposed model almost correctly predicts each person's productivity level. 최근 여러 스마트 장치로부터 취득되는 개인 생체 데이터를 활용하여 개인 맞춤형 환경을 구성하려는 수요가증가하고 있다. 본 논문은 스마트오피스를 환경에서 최적의 업무생산성과 에너지 사용 실내환경 조성을 위한 연구의 일환으로, 실내 조명환경 변화에 따른 재실자의 생체신호를 수집 및 분석하고, 이를 활용하여 기계학습 기반업무생산성 예측모델을 제안한다. 실제 조명환경에 따른 재실자의 데이터를 수집하기 위하여 애리조나 주립대학교학생들을 대상으로 실험을 진행하였다. 생체신호 중 동공크기와 심박수를 고려하였고, 실내 조명환경에 따라 동공크기와 심박수가 영향을 받는 것을 확인하였다. 또한 동공크기와 심박수가 업무생산성과도 상관관계가 있음을 확인하였다. 이를 바탕으로 결정트리 기반의 업무생산성 예측모델을 제안하였고, 트리의 복잡성을 낮추기 위해 제한된 파라미터 범위에서 최적의 성능을 보이는 예측모델을 구성하였다.
효율적인 에너지 사용 및 재실자 개인 맞춤화를 위한 데이터 기반 건물 HVAC 시스템
임세헌,김태근,염동우,윤성국 대한전기학회 2023 전기학회논문지 Vol.72 No.10
The predicted mean vote (PMV) model is widely used to measure thermal comfort for humans, which uses heating, ventilation, and air conditioning (HVAC) systems. However, the PMV model has limitations in satisfying individual person’s thermal comfort. As a result, a recent survey of occupants in buildings showed that the percentage of thermal discomfort is significantly high, despite the active use of the HVAC system. To address this issue, we propose a personalized thermal comfort prediction model based on machine learning that utilizes data from thermal sensation votes, indoor temperature, and humidity. We did an experiment for the data acquisition system, and four students participated. With these data, we develop a personalized thermal comfort prediction model. Among the five machine learning models, i.e., artificial neural network (ANN), linear regression (LR), support vector machine (SVM), ANN is selected showing best performamce. We formulate an optimization problem for the proposed personalized HVAC system, and its solution is derived using a genetic algorithm. The results of the thermal comfort of the personalized model are compared to the PMV model. It shows significant differences between the thermal comfort of the personalized model and the PMV model. Also, the thermal comfort performance and cost are evaluated through a building simulation.