• Atmosphere
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• v.24 no.1
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• pp.17-27
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• 2014

In this study, the observational environment for sunshine duration at Seoul and Daegu Automated Synoptic Observing Systems (ASOSs) was analyzed using a numerical model. In order to analyze the effects of topography and buildings on observational environment for sunshine duration, the model domains including the elevated building and mountainous areas around Seoul and Daegu ASOSs were considered. Three dimensional topography and buildings used as input data for the numerical model were constructed using a geographic information system (GIS) data. Solar azimuth and altitude angles calculated for the analysis period (one-week for each season in 2008) in this study were validated against those by Korea Astronomy and Space Science Institute (KASI). The starting and ending times of sunshine duration observed at ASOSs largely differed from the respective sunrise and sunset times simply calculated using solar angles and information of ASOSs' latitude and longitude, because uneven topography and elevated buildings around ASOSs cut off sunshine duration right after the sunrise and right before the sunset. The model produced the sunshine indices for Seoul and Daegu ASOSs with the time interval of one minute and the period of one week for each season and we compared the hourly averaged indices with those observed at the ASOSs. One week of which the cloudiness is lowest for each season is selected for analysis. Not only the adjacent buildings but also distant buildings and mountain cut off sunshine duration right after the sunrise and right before the sunset. The buildings and topography cutting off sunshine duration were found for each analyzing date. It was suggested that, in order to evaluate the observational environment for sunshine duration, we need to consider even the information of topography and/or building far away from ASOSs. This study also showed that the analyzing method considering the GIS data is very useful for evaluation of observational environment for sunshine duration.

• Journal of the Korean Solar Energy Society
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• v.40 no.1
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• pp.1-13
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• 2020

A study on estimation of the Levelized Cost of Energy (LCOE) was conducted for the Korean onshore wind farms. The LCOE was estimated on the basis of the actual wind farm data from Data Analysis, Retrieval Transfer system (DART) run by Financial Supervisory Service. Recently, social discount rate of Korea dropped from 5.5% to 4.5%, which was taken into account for this study. The onshore wind farms studied accounted for 42% of all the onshore wind farms of South Korea. Capital Expenditure (CapEx) and Operation Expenditure (OpEx) were calculated from the actual data, while Capacity Factors (CFs) were obtained from the wind farms of five provinces. Their distributions were estimated using Maximum Likelihood Estimation method, and then Monte Carlo Simulation (MCS) was performed for estimating LCOE, Levelized Fixed Cost (LFC), and Levelized Variable Cost (LVC). As a result, the LCOEs at the two discount rates, 4.5 and 5.5%, were 142 and 152 $/MWh, respectively, which were lower than that of financially viable onshore wind project of Korea. The 1% drop of social discount rate was estimated to result in a 10 $/MWh decrease in LCOE and a 4 $/MWh in LFC, which can be an advantage for wind project investors.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.222-227
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• 2015

This paper presents the optimal energy generation systems for economical EVs(Electric Vehicles) charging stations located in an island area. The system includes grid electricity, diesel generator and renewable energy sources of wind turbines and PV(Photovoltaic) panels. The independent generation system is designed with data resources such as annual average wind speed, solar radiation and the grid electricity price by calculating system cost under different structures. This sensitive analysis on the varying data resources allows for the configuration of the most economical generation system for charging stations by comparing initial capital, operating cost, NPC(Net Present Cost) and COE(Cost of Energy). Depending on the increase of the grid cost, the NPC variation of the most economical system which includes renewable energy generations and grid electricity can be smaller than those of other generation systems.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.222-224
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• 2009

This paper proposes analysis data of generation efficiency with tracking method for solar tracking. Tracking algorithm of PV generation is divided the sensor method and program method. Generation efficiency is analyzed the three cases 1-high insolation, 2-low insolation, 3-rapidly changing insolation. Proposed data is possible to apply for development of novel algorithm with hybrid tracking method in this paper. Hereby, This paper is proved the benefit of analyzed data.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.5
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• pp.545-554
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• 1999

We would like to develop a short-term model to predict the time-related concentration of ozone whose reaction mechanism is complex. The paper targets Seoul where an ozone alert system has recently been employed. In order to develop a short-term prediction model for ozone, we suggested the Ozone Peak Indicator(OPI), an equivalent of the potential daily maximum ozone concentration, with precursors being the only limiting factor, and we calculated the Ozone Peak Indicarot as OPI={$rac{(O_3)_{max}cdot(H_{eH})_{max}(Rad)_{max}$ to preclude the influence of mixing height and solar radiation on the daily maximum ozone concentration. The OPI on the day of the prediction is to be calcultated by using the relation between OPI and the initial value of precursors. The basic prediction formula for time-related ozone concentration was established as $O_3(1)={(OPI)cdot Rad(t-2)H_{eH}}$, using the OPI, solar radiation two hours before prediction and mixing height. We developed, along with the basic formula for predicting photochemical oxidants, "SEOM"(Seoul Empirical Oxidants Model), a Fortran program that helps predict solar radiation and mixing height needed in the prediction of ozone pollution. When this model was applied to Seoul and an analysis of the correlation between the observed and the predicted ozone concentrations was made through SEOM, there appeared a very high correlation, with a coefficient of 0.815. SEOM can be described as a short-term prediction model for ozone concentration in large cities that takes into account the initial values of precursors, and changes in solar radiation and mixing height. SEOM can reflect the local characteristics of a particular and region can yield relatively good prediction results by a simple data input process.t process.

• Journal of The Korean Astronomical Society
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• v.53 no.4
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• pp.87-98
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• 2020

The Korea Astronomy and Space Science Institute (KASI) has been developing a next-generation coronagraph (NGC) in cooperation with NASA to measure the coronal electron density, temperature, and speed simultaneously, using four different optical filters around 400 nm. KASI organized an expedition to demonstrate the coronagraph measurement scheme and the instrumental technology during the 2017 total solar eclipse (TSE) across the USA. The observation site was in Jackson Hole, Wyoming, USA. We built an eclipse observation system, the Diagnostic Coronal Experiment (DICE), composed of two identical telescopes to improve the signal-to-noise ratio. The observation was conducted at four wavelengths and three linear polarization directions in the limited total eclipse time of about 140 seconds. We successfully obtained polarization data for the corona but we were not able to obtain information on the coronal electron temperature and speed due to the low signal-to-noise ratio of the optical system and strong emission from prominences located at the western limb. In this study, we report the development of DICE and the observation results from the eclipse expedition. TSE observation and analysis with our self-developed instrument showed that a coronagraph needs to be designed carefully to achieve its scientific purpose. We gained valuable experience for future follow-up NASA-KASI joint missions: the Balloon-borne Investigation of the Temperature and Speed of Electrons in the Corona (BITSE) and the COronal Diagnostic EXperiment (CODEX).

• KIEAE Journal
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• v.13 no.2
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• pp.77-84
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• 2013

Schools are one of favorable public buildings for Renewable Energy(RE) systems due to site conditions and their energy demand profiles(e.g. daytime-based use of hot water and heating/cooling). Although the government encourages schools to be equipped with RE systems, the adoption of RE systems in existing energy supply systems faces technical and financial barriers. For example, when installing a RE-based combination system(RECS) to meet the energy demand at various school scales, identifying cost effective combination of capacities of the RECS is not trivial since it usually requires technically intensive work including detailed simulation and demand/supply analysis with extensive data. This kind of simulation-based approaches is hardly implementable in practice. To address this, a simpler and applicable decision-supporting method is suggested in this study. This paper presents a simplified model in support of decision-making for optimal capacities of RECS within given budget scales and schools sizes. The proposed model was derived from detailed simulation results and statistical data. Using this model, the optimal capacities of RECS can be induced from the number of classes in a school.

• Proceedings of the KIEE Conference
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• summer
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• pp.1483-1485
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• 2004

Photovoltaic(PV) technology is a popular part of building vocabulary. It can be used today on both existing and new buildings. Its use in the building envelope is very varied and open ways, such as roofing materials, facades, skylights and shading systems, for creative designers. So, to activate this systems demand appropriate sources of information, performance data of elements and design tools offering architects and designer. Therefore this paper describe application elements for BIPV system and then predict improvement in the generated electric power performance of balcony BIPV system.