In this paper, characteristic of BIPV power in office building installed curtain wall was studied. BIPV of target building is attached in south facade of the building, whose maximum power is 5.4 kW. BIPV power was monitored from 1. March to 15. April. Using EnergyPlus as a simulation tool, baseline model was built. BIPV power of the baseline model was validated with monitored power. Cv(RMSE) of the baseline model was 27% in one week(4 ~ 10. April). Based on baseline model, case study was carried out adding same type of BIPV to the other sides of facades. BIPV in south facade produces 35% of total produced BIPV power. The amount of produced power was varied by weather conditions such as rains, clouds and temperature. In sunny day, the amount of produced BIPV power was 49 kWh which was about half of lights electric consumption in one day. Also, produced BIPV power through weekend was 75 kWh which can be used next weekdays using a storage battery.
We investigated the prevalence of infectious duck diseases using 156 ducks reared in 18 farms of western Gyeong-nam province. As a result, duck viral hepatitis (12.8%), colibacillosis (7.1%), and fungal disease (9.0%) were detected. However, avian influenza and riemerellosis were not detected. During autopsies, we could grossly observed red swollen liver (12.8%), petechial or ecchymotic hemorrhage on liver (11.5%), and fibrinous perihepatitis (9.0%). Gray-white necrotic spot (23.1%), swollen spleen (22.8%), swollen kidney (20.5%), hyperemia or hemorrhage on tracheal mucous membrane (8.3%), and nodule in long or air sac (9.0%) were also found.
전통민가는 한 지역의 미기후학적특성 및 자연환경에 잘 적응 발전하여 온 건축물이라 할 수 있다. 본 연구에서는 조선시대에 건축된 전통민가(기와집.초가집)를 대상으로 하여 그 지역의 기후 특성과 건축물내의 정량적이며 물리적인 온열환경을 파악하기 위하여 실내.외 온열환경요소및 벽체의 열류를 계절별로 측정하여 대상민가의 미기후 특성과 각 가옥 구조체의 열적 성능을 검토.분석하였다.본 논문의 대상지역은 충청남도 아산군 송악면 외암리이며 측정 계절은 춘계와 하계이다. 측정 결과를 분석하여 양 가옥 실내의 온열환경범위를 수정유효온도(C.E.T)를 사용하여 파악하였으며 이를 근래의 인체 쾌적범위와 비교.검토하였다.
As part of global efforts to response climate change and reduce carbon emissions, various efforts have been made to reduce energy consumption, and recently many efforts to improve energy efficiency using science and technology have been paid much attention to. In the area of energy efficiency improvement, data science will play an important role in generating timely insights so that PDCA activities(Plan-Do-Check-Action) for energy savings can be performed quickly. In this study, we will analyze what kind of data science techniques are utilized for improving energy efficiency of various facilities with what purpose, and discuss what should be considered when conducting analysis.
Exhibition facilities are known as an internal load dominated-type building, as compared to other buildings. For the energy efficiency improvement of the subject large commercial building complex, it is valuable to monitor and manage energy performance data through FMS or BAS. Large amount of hourly raw data sets of the entire cooling season were processed and analyzed for the research purpose. The study objective is to investigate correlations between energy consumptions and various field conditions including weather data. Non-energy related data such as leased floor area was identified as a good indicator for the load prediction. The results showed the acceptable load characteristics and expectable correlations between factors and implies the necessity of extra valuable data monitoring such as accurate occupancy and use information.
This study is to propose a method of predicting the number of present occupants using Wi-Fi, a sensor within the building. The accuracy and correlation of the estimated number of occupant by proposing method and schedule for generally used in a building energy simulation were compared with real occupant schedule. The validation of the proposed method has been made. MAC(Media Access Control) address and Wi-Fi was used to predict the number of present occupants, and data were collected and analyzed for 20 days. The energy consumption for air conditioning and the indoor CO₂ concentration were analyzed to examine the influence of the number of present occupant using EnergyPlus. The results showed that the method considering the regression coefficient on the number of occupant accessing Wi-Fi is the most close to the real values and occupants, air-conditioning system energy use and CO₂ concentration.
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