• 농업생명과학연구
• /
• 제45권4호
• /
• pp.135-141
• /
• 2011

본 연구에서는 주간동안 온실 내에서 발생되는 잉여 태양에너지를 분석하고, 또한 잉여 태양에너지의 적정 축열 시스템 설계에 필요한 기초자료를 제공할 목적으로 수행하였다. 분석에 이용된 기상자료는 표준기상년 데이터로서 이용하여 국내 주요 지역을 대상으로 온실 형태별로 잉여 태양에너지를 분석하였을 뿐만 아니라 소요 난방에너지 등도 분석 및 검토하였다. 이상의 결과를 요약하면 다음과 같다. 9개 지역을 대상으로 지역별 잉여 태양에너지를 대해 분석한 결과, 난방에너지 대비 잉여 태양에너지 비율은 온실 형태별로 각각 약 212.0~228.0%로서 제주가 가장 높게 나타났다. 그 다음으로 부산, 광주, 진주, 대구, 대전, 전주, 수원, 및 대관령 순으로 나타났다. 그리고 온실 형태에 관계없이 몇 몇 지역을 제외하면 잉여 태양에너지만으로 소요 난방에너지를 거의 대체할 수 있을 것으로 판단되었다.

• 한국환경과학회지
• /
• 제15권12호
• /
• pp.1125-1139
• /
• 2006

The cluster analysis of diurnal precipitation patterns is performed by using daily precipitation of 59 stations in South Korea from 1973 to 1996 in four seasons of each year. Four seasons are shifted forward by 15 days compared to the general ones. Number of clusters are 15 in winter, 16 in spring and autumn, and 26 in summer, respectively. One of the classes is the totally dry day in each season, indicating that precipitation is never observed at any station. This is treated separately in this study. Distribution of the days among the clusters is rather uneven with rather low area-mean precipitation occurring most frequently. These 4 (seasons)$\times$2 (wet and dry days) classes represent more than the half (59 %) of all days of the year. On the other hand, even the smallest seasonal clusters show at least $5\sim9$ members in the 24 years (1973-1996) period of classification. The cluster analysis is directly performed for the major $5\sim8$ non-correlated coefficients of the diurnal precipitation patterns obtained by factor analysis In order to consider the spatial correlation. More specifically, hierarchical clustering based on Euclidean distance and Ward's method of agglomeration is applied. The relative variance explained by the clustering is as high as average (63%) with better capability in spring (66%) and winter (69 %), but lower than average in autumn (60%) and summer (59%). Through applying weighted relative variances, i.e. dividing the squared deviations by the cluster averages, we obtain even better values, i.e 78 % in average, compared to the same index without clustering. This means that the highest variance remains in the clusters with more precipitation. Besides all statistics necessary for the validation of the final classification, 4 cluster centers are mapped for each season to illustrate the range of typical extremities, paired according to their area mean precipitation or negative pattern correlation. Possible alternatives of the performed classification and reasons for their rejection are also discussed with inclusion of a wide spectrum of recommended applications.

• Current Photovoltaic Research
• /
• 제5권1호
• /
• pp.38-42
• /
• 2017

PV system output is determined according to the weather conditions, the azimuth and tilt angle. Weather conditions are changing every moment and it seems to vary according to the daily, monthly, and annual basis. The azimuth and tilt angle is decided along the site conditions for the PV system installation. This paper analyzed the PV system output through the changing the weather conditions, the azimuth, and tilt angle. We compared the TMY data and analysis of the two major weather institutes which are KMA and METEONORM. PV system output trend were analyzed by changing the azimuth and tilt angle. We used simulation tool, which is named PVsyst for the entire PV system analysis.

• Ocean and Polar Research
• /
• 제37권3호
• /
• pp.179-188
• /
• 2015

This study was carried out to determine the optimal number of ARGO floats in the East Sea in order to maximize their applications. The dominant spatio-temporal scale, size of the domain, and the typical float lifetimes in the East Sea were taken into consideration. The mean spatial de-correlation scale of temperature on isobaric surfaces reaches about 60 km. The minimum necessary number of floats is about 82 on average in order to secure independent ARGO profiles with the de-correlation scale. Considering the float lifetimes, about 27 floats per year should be deployed to maintain the 82 ARGO float array every year. To obtain spatially uniform distribution of ARGO float data, mean residence time and dispersion rate (basin area/residence time) of ARGO floats were evaluated in each basin of the East Sea. A faster (slower) dispersion rate requires more (less) ARGO floats to maintain the spatially uniform number of floats. According to the analysis, it is likely that the optimal ratio of the number of floats for each basin is 1:2:4 corresponding to Ulleung Basin:Yamato Basin:Japan Basin. In order to maintain relatively uniform ARGO observing networks, it is necessary to establish a long-term plan for deployment strategy based on float pathways and the dispersion rate parameters estimated by using currently active ARGO float trajectory data as well as reanalysis data.

• 한국태양에너지학회 논문집
• /
• 제39권2호
• /
• pp.33-43
• /
• 2019

In this study, reflecting long-term climate characteristics, we analyzed electricity generation and generation characteristics of 3kWp PV system, which was semi-integrated with air duct behind. Using PVsyst as a simulation analysis tool, we inputted "National reference standard weather data" of 16 regions as a typical climatic data. The result is summarized as follows: First, the national average annual electricity generation was 1,312 kWh/kWp (StDev, ${\sigma}=71$). It was most abundant in Mokpo with 1,434 kWh/kWp, which was average 21% greater than the lowest with 1,165 kWh/kWp in Seoul and 1,197 kWh/kWp in Jeju. National average daily generating time based on STC was 3.6 hours (${\sigma}=0.43$), and that of Mokpo and Seoul was 3.9 and 3.2 hours respectively. Second, Jeju showed the great difference of annual monthly generation by month (annual average = 99.7 kWh/kWp, ${\sigma}=25.5$), while Jinju showed the smallest difference (annual average = 115.5 kWh/kWp, ${\sigma}=10.6$). Generation in Jeju was at the largest in April with 132.2 kWh/kWp, which was 2.3 times greater than the lowest 55.2 kWh/kWp in January. However, generation in Jinju was at the largest in March with 129.3 kWh/kWp, which was only 1.3 times greater than the lowest 101.1 kWh/kWp in June. Third, the annual average PR was the highest in Incheon with 85.8% and the lowest in Jeju with 83.2%. PR of Mokpo was 84.3%, which was lower than that of national average.

• 농업생명과학연구
• /
• 제46권1호
• /
• pp.195-206
• /
• 2012

본 연구는 주간동안 온실 내에서 발생되는 잉여 태양에너지의 적정 축열 시스템 설계에 필요한 기초자료를 제공할 목적으로 확보한 표준기상년 데이터를 이용하여 설정온도별로 잉여 태양에너지를 분석하였다. 주야간 설정온도를 단계별로 증가($15{\sim}19^{\circ}C$)시킨 경우, 온실형태와 지역별로 잉여 태양에너지는 0.2~6.9%정도 증가하여 그 증가폭은 미미하지만 다소 완만히 증가함을 알 수 있었다. 그리고 소요 난방에너지는 29.7~50.0%정도 증가하여 잉여 태양에너지의 증가율 보다 훨씬 큰 폭으로 증가하는 것을 알 수 있었다. 환기 설정온도를 단계별로 증가 (저속 $25{\sim}29^{\circ}C$, 고속 $27{\sim}31^{\circ}C$)시킨 경우, 자동화 온실은 지역별로 잉여 태양에너지는 9.9~35.6%정도로 감소하는 것으로 나타났다. 그리고 단동형 온실은 지역별로 5.1~13.4%정도로 감소하는 것으로 나타나 자동화 온실에 비해 감소의 폭이 상대적으로 작았다. 또한 소요 난방에너지는 온실형태 및 지역별로 다소 증가하거나 감소하는 경우도 있었지만, 그 영향은 아주 미미한 것으로 나타났다.

• 한국입자에어로졸학회지
• /
• 제7권2호
• /
• pp.59-68
• /
• 2011

The objective of the study was to investigate the main factors that contribute the variation of $PM_{10}$ concentration of Seoul and to quantify their effects using generalized additive model (GAM). The analysis was performed with 3 year air pollution data (2004~2006) measured at 27 urban sites and 7 roadside sites in Seoul, a background site in Gangwha and a rural site in Pocheon. The diurnal variation of urban $PM_{10}$ concentrations of Seoul showed a typical bimodal pattern with the same peak times as that of roadside, and the maximum difference of $PM_{10}$ level between urban and roadside was about $14{\mu}g/m^{3}$ at 10 in the morning. The wind direction was found to be a major factor that affects $PM_{10}$ level in all investigated areas. The overall $PM_{10}$ level was reduced when air came from east, but background $PM_{10}$ level in Gangwha was rather higher than the urban $PM_{10}$ level in Seoul, indicating that the $PM_{10}$ level in Gangwha is considerably influenced by that in Seoul metropolitan area. When hourly variations of $PM_{10}$ were analyzed using GAM, wind direction and speed explained about 34% of the variance in the model where the variables were added as a 2-dimensional smoothing function. In addition, other variables, such as diurnal variation, difference of concentrations between roadside and urban area, precipitation, month, and the regression slope of a plot of carbon monooxide versus $PM_{10}$, were found to be major explanatory variables, explaining about 64% of total variance of hourly variations of $PM_{10}$ in Seoul.

• 수산해양기술연구
• /
• 제46권1호
• /
• pp.70-81
• /
• 2010

A mathematical model is formulated to study the effect of wall mass on the thermal performance of four different houses of different construction. This analytical study was motivated by the experimental work of Burch et al. An analytical solution of one -dimensional, linear, partial differential equation for wall temperature profiles and room air temperatures is obtained using the Laplace transform method. Typical Meteorological Year data are processed to yield hourly average monthly values. These discrete data are then converted to a continuous, time dependent form using a Fast Fourier Transform method. This study is conducted using weather data from four different locations in the United States: Albuquerque, New mexico; Miami, Florida; Santa Maria, California; and Washington D.C. for both winter and summer conditions. A computer code is developed to calculate the wall temperature profile, room air temperature, and energy consumption loads. Three sets of results are calculated one for no auxiliary energy and two for different control mechanism -- an on-off controller and a proportional controller. Comparisons are made for the cases of two controllers. Heavy weight houses with insulation in mild weather areas (such as August in Santa Maria, California) show a high comfort level. Houses using proportional control experience a higher comfort level in comparison to houses using on-off control. The result shows that there is an effect of mass on the thermal performance of a heavily constructed house in mild weather conditions.

• 수산해양기술연구
• /
• 제47권1호
• /
• pp.46-58
• /
• 2011

A steady-state analysis and a simple dynamic model as simplified methods are developed, and results of energy consumption loads are compared with results obtained using computer to evaluate the analytical solution. Before obtaining simplified model a mathematical model is formulated for the effect of wall mass on the thermal performance of four different houses having various wall construction. This analytical study was motivated by the experimental work of Burch et al. An analytical solution of one-dimensional, linear, partial differential equation for wall temperature profiles and room air temperatures is obtained using the Laplace transform method. Typical Meteorological Year data are processed to yield hourly average monthly values. This study is conducted using weather data from four different locations in the United States: Albuquerque, New mexico; Miami, Florida; Santa Maria, California; and Washington D.C. for both winter and summer conditions. The steady state analysis that does not include the effect of thermal mass can provide an accurate estimate of energy consumption in most cases except for houses #2 and #4 in mild weather areas. This result shows that there is an effect of mass on the thermal performance of heavily constructed house in mild weather conditions. The simple dynamic model is applicable for high cycling rates and accurate values of inside wall temperature and ambient air temperature.

• 한국대기환경학회지
• /
• 제22권3호
• /
• pp.297-307
• /
• 2006

During January $2002{\sim}April\;2004$, hydrogen peroxide ($H_{2}O_{2}$) measurements were performed at the campus of Korea University, which is located in the northeastern part of Seoul. Gas phase hydroperoxide was collected in aqueous solution and separated by HPLC. Concentrations were determined by fluorescence using postcolumn enzyme derivatization. This measurement system was improved to be run automatically from sample collection at every 10 minutes through chemical analysis for data collection. Detection limits of $H_{2}O_{2}$ is $10{\sim}17\;pptv$, and the overall uncertainty of the measurements is better than 8%. Two-year measurements of $H_{2}O_{2}$ show typical seasonal variations. Concentrations of $H_{2}O_{2}$ were higher during $June{\sim}October$ and lower during $January{\sim}February$. Maximum concentration of 1-hour averaged $H_{2}O_{2}$ was 6.5 ppbv, which was observed in August and September. In general $H_{2}O_{2}$ concentrations were well correlated with $O_{3}$ concentrations and largely affected by meteorological factors such as temperature and wind direction.