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- 저자 : 채영태(Chae, Young-Tae) (검색결과 110 건)
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채영태(Young Tae Chae) 한국생활환경학회 2016 한국생활환경학회지 Vol.23 No.5
It has been interested in appling smart window system to buildings in order to control solar transmittance by active way. The performance of control strategies on the smart window system, especially electrochormic window (ECW), have not fully evaluated in the perspective of building energy consumption. The presented article provides potential control parameters and logics to operate ECW. It also evaluates building energy performance by the developed ECW controls conducting annual building energy simulation for a medium size office building model. Five control parameters are derived from time scheduling (SPC), solar irradiance (IRD), outdoor air temperature (OAT), cooling demand (CMD), and room air temperature(RAT). Each control parameter has ranges or target values to activate ECW operation. The result shows the control methods can avoid solar transmittance by 90% thorough South, East, and West in the highest solar radiation day in Inchoen, Korea. ECW operation can reduce cooling equipment sizing by 20% in approximation and save annual HVAC energy consumption ranges from 11.22% to 20.70% by the control methods. The RAT would be the most effective control parameter from this study because it outperforms other parameters on annual HVAC energy saving and investment of system installation.
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Electrochromic 창호 적용시 지역별 건물 냉난방 에너지 소비량 절감성능
신재윤(Shin Jae-Yoon),채영태(Chae Young Tae) 한국태양에너지학회 2018 한국태양에너지학회 논문집 Vol.38 No.5
The most crucial point of reducing building energy is application of high performance envelope. The amount of heat exchange through window is highest in comparison of other envelopes so that heat exchange through window influence directly with building energy consumption. The window energy performance can be define with thermal, leakage and optical performance. In previous study we can confirmed that not only thermal performance but also optical performance are considered, 11% to 15% of building energy consumption can be reduced. Smart window system has potential of energy saving so that many industry field use smart window system including architectural area and these aspect causes smart window market continuous growth year by year. In this study, building energy consumption has been analyzed which consist of smart window that dynamically control optical states. The consideration of standard commercial building model for research, the reference medium size commercial building model of DOE (Department Of Energy, USA) has been used. The building energy simulation result of 4 axis in 8 regions in Korea shows 8% to 22% reduction of building energy consumption by application of smart window system.
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기계학습을 활용한 기상예측자료 기반 태양광 발전량 예측 향상기법
정진화(Jin-Hwa Jeong),채영태(Young-Tae Chae) 한국생활환경학회 2018 한국생활환경학회지 Vol.25 No.1
This study investigated a selection of machine learning model to forecast electric power output from photovoltaic arrays based on forecasted weather data and historic solar radiation data. It tested two approaches to improve forecasting accuracy of power output with three typical machine learning algorithms such as Random Forest(RF), Artificial Neural Network(ANN), and Support Vector Machine(SVM). A forecasting power output was conducted with conventional weather forecasting data from national weather service which does not include solar radiation. The other approach has two steps, forecasting solar radiation with weather forecasting data and historic solar radiation data then it forecasts the electric power output of photovoltaic arrays. It has been studied the importance variables incorporated with the power output forecasting. The results show that the forecasting accuracy of the power output improves by using forecasted solar radiation data and Random Forest outperforms on this power output forecasting problem among other machine learning algorithms.
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건물유형별 에너지소비 예측성능 향상을 위한 변수중요도 및 기계학습모델 평가
정진화(Jeong, Jin-Hwa),채영태(Chae, Young-Tae) 한국건축친환경설비학회 2017 한국건축친환경설비학회 논문집 Vol.11 No.6
The optimal machine learning model depends on building types was selected by comparing and analyzing short term load forecasting (STLF) performance of primary school and commercial reference building based on 4 machine learning models such as ANN, SVM, CHAID, and, RF. The research consists of data collection-storage, data analysis, meteorological variables extraction, energy consumption forecasting and analysis on typical primary school and commercial building energy model. TMY (Typical Meteorological Year) of Incheon, Korea was applied and based on weather forecasting data provided by the KMA (Korea Meteorological Agency). In case of building energy consumption data, primary school and medium commercial reference building energy consumption data by on EIA’s Commercial Buildings Energy Consumption Survey (CBECS) were used. Key weather variables were extracted for each machine learning model between the input variables and the output which is building energy consumption in 15 minutes interval. Finally, forecasting of energy consumption on different building types conducted a comparative analysis of the forecasting performance of building energy consumption based on 4 machine learning models using optimal input variables. The results shows ANN model outperforms other models with 5.44% of CV (RMSE) for 7 days school building energy forecasting trained 8 weeks prior data. Whereas, RF model performs better than the others with 10.96% of CV (RMSE). It may be concluded that the priority of variables which have impacts on energy consumption is important and the most suitable model for energy forecasting is different by the building types.
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