• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.723-730
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• 2016

Solar cells in a PV module are connected in series and parallel to produce a higher voltage and current. The PV module has performance characteristics depending on solar radiation and temperature. In addition, the PV system causes power loss by special situations, including the shadows of the surrounding environment, such as nearby buildings and trees. In other words, an increase in power loss and a decrease in life cycle can occur because of the partial shadow and hot-spot effect. Therefore, this paper proposes the optimal configuration algorithm of a bypass diode to improve the output of a PV module and one of a PV array to minimize the loss of the PV array. In addition, this paper presents a model of a PV module and PV array based on the PSIM S/W. The simulation results confirmed that the proposed optimal configuration algorithms are useful tools for improving the performance of PV system.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1240-1243
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• 2008

제주도는 연평균 강우량이 1,975mm에 달하는 우리나라 최대의 다우지역이며 투수성이 좋은 다공질 화산암류 및 화산회토로 이루어져 있어 총 강우량의 48.5%에 이르는 빗물이 지하로 침투하여 대부분의 하천들은 건천을 이루고 있다. 제주도의 143개 하천 중 6개의 하천을 제외한 전 하천들은 건천의 형태를 이루고 있어 지표수의 발달이 매우 빈약하다. 본 연구에서는 장기 강우-유출 모형인 SWAT(Soil and Water Assessment Tool) 모형을 적용하여 제주도 주요하천의 유출량을 산정하고자 한다. 143개 하천 중 제주도 동부유역의 천미천과 북부유역의 외도천을 연구대상유역으로 선정하여 SWAT 모형을 적용하였다. 연구 대상유역에 대한 SWAT 모형의 입력자료인 수문 기상자료(Precipitation, Solar Radiation, Wind Speed, Climate, Humidity)와 지형자료(DEM(Digital Elevation Model), Land Use, Soil Type)를 구축하였으며, 동시에 모형의 보정 및 검증을 위하여 천미천 외도천 유역의 실측 유출자료를 수집하여 정리하였다. 모델의 입력자료를 구축하고 SWAT 모형을 이용하여 천미천 외도천 유역의 유출 모의를 하였고, 유출 모의 결과를 바탕으로 하여 수문관련 매개변수들의 민감도 분석을 하였으며, 민감도 분석을 통하여 보정을 수행하였다. 보정을 수행한 결과를 바탕으로 하여 천미천 외도천의 유출모의 결과를 분석하였으며, 향후 제주도에 필요한 연구결과 활용방안에 대하여 검토한 이상의 결과들로부터 제주도 하천에 대하여 SWAT모형을 적용한 결과 장기 일 유출량 모의에 대하여 전체적으로 우수한 결과를 보이고 있다. 향후 많은 보다 많은 유출량 자료를 확보하여 본 연구의 결과와 비교 검정하여 SWAT 모형을 구축한다면 제주도 하천의 장기 일 유출량 모의를 할 수 있을 것이라 판단된다.

• KIEAE Journal
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• v.13 no.5
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• pp.89-96
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• 2013

Green roofs can reduce surface water runoff, provide a habitat for wildlife moderate the urban heat island effect, improve building insulation and energy efficiency, improve the air quality, create aesthetic and amenity value, and preserve the roof's waterproofing. Green roofs are mainly divided into three types : intensive, simple-intensive, and extensive. Especially, extensive roof environment is a harsh one for plant growth; limited water availability, wide temperature fluctuations, high exposure to wind and solar radiation create highly stressed environment. This study, aimed at extensive green roof, was carried out on the rooftop of the library at Seoul Women's Univ. from October to November, 2012 and from March to August, 2013. To suggest the most effective vegetation model for biodiversity and heat island mitigation, surface temperatures were monitored by each vegetation model. We found that herbaceous plants of Aster sphathulifolius, Aceriphyllum rossii and Belamcanda chinensis, shrub of Syringa patula 'Miss Kim', Thymus quinquecostatus var. japonica, Sedum species can mixing each other. Among them, the vegetation models including Sedum takesimense, Aster sphathulifolius, Thymus quinquecostatus var. japonica was more effective on the surface temperature mitigation, because the species have the tolerance and high ratio of covering, and also in water. Especially, in the treatment of bark mulching, they helped to increase the temperature of vegetation models. In the case of summer, temperature mitigation of vegetation models were no significant difference among vegetation types. Compared to surface temperature of June, July and August were apparent impact of temperature mitigation, it shows that temperature mitigation are strongly influenced by substrate water content.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.1
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• pp.61-69
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• 2004

A long-term growth simulation was performed at 99 land units in Yeoncheon county to test the potential adaptability of each land unit for growing soybean cultivars. The land units for soybean cultivation(CZU), each represented by a geographically referenced land patch, were selected based on land use, soil characteristics, and minimum arable land area. Monthly climatic normals for daily maximum and minimum temperature, precipitation, number of rain days and solar radiation were extracted for each CZU from digital climate models(DCM). The DCM grid cells falling within a same CZU were aggregated to make spatially explicit climatic normals relevant to the CZU. A daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CROPGRO-soybean model suitable for 2 domestic soybean cultivars were derived from long-term field observations. Three foreign cultivars with well established parameters were also added to this study, representing maturity groups 3, 4, and 5. Each treatment was simulated with the randomly generated 30 years' daily weather data(from planting to physiological maturity) for 99 land units in Yeoncheon to simulate the growth and yield responses to the inter-annual climate variation. The same model was run with input data from the Crop Experiment Station in Suwon to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for evaluation. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific cultivar. A computer program(MAPSOY) was written to help utilize the results in a decision-making procedure for agrotechnology transfer. transfer.

• Journal of Astronomy and Space Sciences
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• v.14 no.1
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• pp.136-141
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• 1997

To calculate the position and velocity of the artificial satellite precisely, one has to build a mathematical model concerning the perturbations by understanding and analysing the space environment correctly and then quantifying. Due to these space environment model, the total acceleration of the artificial satellite can be expressed as the 2nd order differential equation and we build an orbit propagation algorithm by integrating twice this equation by using the Cowell's method which gives the position and velocity of the artificial satellite at any given time. Perturbations important for the orbits of geostationary spacecraft are the Earth's gravitational potential, the gravitational influences of the sun and moon, and the solar radiation pressure. For precise orbit propagation in Cowell' method, 40 x 40 spherical harmonic coefficients can be applied and the JPL DE403 ephemeris files were used to generate the range from earth to sun and moon and 8th order Runge-Kutta single step method with variable step-size control is used to integrate the the orbit propagation equations.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Economic and Environmental Geology
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• v.39 no.3 s.178
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• pp.255-268
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• 2006

Recent 22-year (1981-2002) meteorological data of 58 Korea Meteorological Adminstration (KMA) station were analyzed to investigate spatial and temporal variation of surface air temperature (SAT) and ground surface temperature (GST) in Korea. Based on the KMA data, multiple linear regression (MLR) models, having two regression variables of latitude and altitude, were presented to predict mean surface air temperature (MSAT) and mean ground surface temperature (MGST). Both models showed a high accuracy of prediction with $R^2$ values of 0.92 and 0.94, respectively. The prediction of MGST is particularly important in the areas of geothermal energy utilization, since it is a critical parameter of input for designing the ground source heat pump system. Thus, due to a good performance of the MGST regression model, it is expected that the model can be a useful tool for preliminary evaluation of MGST in the area of interest with no reliable data. By a simple linear regression, temporal variation of SAT was analyzed to examine long-term increase of SAT due to the global warming and the urbanization effect. All of the KMA stations except one showed an increasing trend of SAT with a range between 0.005 and $0.088^{\circ}C/yr$ and a mean of $0.043^{\circ}C/yr$. In terms of meteorological factors controlling variation of GST, the effects of solar radiation, terrestrial radiation, precipitation, and snow cover were also discussed based on quantitative and qualitative analysis of the meteorological data.

• Journal of Bio-Environment Control
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• v.25 no.2
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• pp.123-132
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• 2016

The importance of energy saving technology for managing greenhouse was recently highlighted. For practical use of energy in greenhouse, it is necessary to simulate energy flow precisely and estimate heating/cooling loads of greenhouse. So the main purpose of this study was to develope and to validate greenhouse energy model and to estimate annual/maximum energy loads using Building Energy Simulation (BES). Field experiments were carried out in a multi-span plastic-film greenhouse in Jeju Island ($33.2^{\circ}N$, $126.3^{\circ}E$) for 2 months. To develop energy model of the greenhouse, a set of sensors was used to measure the greenhouse microclimate such as air temperature, humidity, leaf temperature, solar radiation, carbon dioxide concentration and so on. Moreover, characteristic length of plant leaf, leaf area index and diffuse non-interceptance were utilized to calculate sensible and latent heat exchange of plant. The internal temperature of greenhouse was compared to validate the greenhouse energy model. Developed model provided a good estimation for the internal temperature throughout the experiments period (coefficients of determination > 0.85, index of agreement > 0.92). After the model validation, we used last 10 years weather data to calculate energy loads of greenhouse according to growth stage of greenhouse crop. The tendency of heating/cooling loads change was depends on external weather condition and optimal temperature for growing crops at each stage. In addition, maximum heating/cooling loads of reference greenhouse were estimated to 644,014 and $756,456kJ{\cdot}hr^{-1}$, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.1-16
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• 2021

Global warming due to climate change is expected to significantly affect the hydrological cycle of agriculture. Therefore, in order to predict the magnitude of climate impact on agricultural water resources in the future, it is necessary to estimate the water demand for irrigation as the climate change. This study aimed at evaluating the future changes in water demand for irrigation under two Shared Socioeconomic Pathways (SSPs) (SSP2-4.5 and SSP5-8.5) scenarios for paddy rice in Gimje, South Korea. The APEX-Paddy model developed for the simulation of paddy environment was used. The model was calibrated and validated using the H₂O flux observation data by the eddy covariance system installed at the field. Sixteen General Circulation Models (GCMs) collected from the Climate Model Intercomparison Project phase 6 (CMIP6) and downscaled using Simple Quantile Mapping (SQM) were used. The future climate data obtained were subjected to APEX-Paddy model simulation to evaluate the future water demand for irrigation at the paddy field. Changes in water demand for irrigation were evaluated for Near-future-NF (2011-2040), Mid-future-MF (2041-2070), and Far-future-FF (2071-2100) by comparing with historical data (1981-2010). The result revealed that, water demand for irrigation would increase by 2.3%, 4.8%, and 7.5% for NF, MF and FF respectively under SSP2-4.5 as compared to the historical demand. Under SSP5-8.5, the water demand for irrigation will worsen by 1.6%, 5.7%, 9.7%, for NF, MF and FF respectively. The increasing water demand for irrigating paddy field into the future is due to increasing evapotranspiration resulting from rising daily mean temperatures and solar radiation under the changing climate.