• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.127-132
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• 2009

A Microsoft Access application program is developed to calculate energy demands for a Community Energy System (CES) composed of various types of buildings. The field-measured heating, hot water, cooling, and electricity energy consumptions for 14 types of building are systematically organized in forms of database and hourly loads for a span of year (8760 hours) are generated through an automated statistical procedure. User-friendly standard windows interfaces are provided to assist non-expert end users.

• Journal of the Korean Solar Energy Society
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• v.29 no.3
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• pp.37-44
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• 2009

The field-surveyed and measured energy consumption data is processed to develop building energy demand models for heating, hot water, cooling, and electricity. The load models are systematically organized as a database and hourly loads for a span of year (8760 hours) are generated by the program. Rased on those models a Microsoft Access application program is developed to calculate energy demands for a Community Energy System (CES) composed of 17 types of buildings. User-friendly interfaces are provided to assist non-expert end users and necessary tools to link the calculation results to subsequent coagulations such as operation simulation or economic assessment.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.93-112
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• 2019

In this paper, a very new web-based software for renewable energy system (RES) design and economic evaluation was introduced. This solution would provide the precise RES estimation service including not only photovoltaic (PV), wind turbine (WT) and fuel cell (FC) individually but also energy storage system (ESS) as combined forms with PV or WT. The three reasons why we ought to develop it are: First, the standardized tool suitable to the domestic environment for estimating power generation from RES facilities and economic evaluation is required. Secondly, the standardized tool is needed to spread domestic RES supply policy and to promote the new industry in the micro-grid field. The last, the reliability of economic evaluation should be enhanced more for new facilities. To achieve those aims, the weather database of one hundred locations have established and the RES facility database has also constructed. For the energy management, mathematical models for PV, WT, ESS and FC were developed. As a final phase, the analytical process to evaluate economics has performed with field data verification.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• Journal of Astronomy and Space Sciences
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• v.25 no.2
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• pp.139-148
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• 2008

It has been known that high-speed solar wind streams associated with coronal holes lead to quasi-periodic substorms that occur approximately every $2{\sim}4$ hours. In this paper we examined 222 repetitive substorms that occurred during high-speed stream periods in July through December in 2003 to quantitatively determine a range of energy input from the solar wind into the magnetosphere between two consecutive substorms. For this study, we have used the Akasofu ${\varepsilon}$-parameter to time-integrate it for the interval between two consecutive substorms, and have applied this method to the 222 substorms. We find that the average amount of solar wind input energy between two adjacent substorms is $1.28{\times}10^{14}J$ and about 85% out of the 222 substorms occur after an energy input of $2{\times}10^{13}{\sim}2.3{\times}10^{14}J$. Based on these results, we suggest that it is not practical to predict when a sub storm will occur after a previous one occurs purely based on the solar wind-magnetosphere energy coupling. We provide discussion on several possible factors that may affect determining substorm onset times during high-speed streams.

• Journal of the Korean Solar Energy Society
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• v.26 no.3
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• pp.119-125
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• 2006

The impacts of climate changes on building energy demand were investigated by means of the degree-days method. Future trends for the 21st century was assessed based on climate change scenarios with 7 global climate models(GCMs). We constructed hourly weather data from monthly temperatures by Trnsys 16. A procedure to estimate heating degree-days (HDD) and cooling degree-days (CDD) from monthly temperature data was developed and applied to three scenarios for Inchon. In the period 1995-2080, HDD would fall by up to 70%. A significant increase in cooling energy demand was found to occur between 1995-2004(70% based on CDD). During 1995-2080, CDD would Increase by up to 120%. Our analysis shows widely varying shifts in future energy demand depending on season. Heating costs in winter will significantly decrease whereas more expensive electrical cooling energy will be needed.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.154-159
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• 2008

Daylight is an important component for human and energy saving. Also, available daylight in inside provides positive influence on psychological and physiological aspects as well as good visual environment. It is important to lighting design for office building not only designing for artificial lighting but also using daylight for energy savings. Therefore, lighting designers and architectures must consider the effects of the daylight for human environment and energy savings. The BIN Method is one of the methods to predict energy savings using computer simulation but it spends more time than expectation. So, this study performs to simulate a simple space using the RADIANCE for examination and simplification of the BIN Method.

• Journal of the Korean Solar Energy Society
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• v.24 no.4
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• pp.35-43
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• 2004

Illuminance and luminous levels in a small office space due to daylight were calculated to analyze the impact of daylight on contrast and lighting energy savings. Computer simulations were performed for four blind conditions under a clear sky condition. The blind conditions significantly impacted the illuminance an4 luminance level. Visual performance scores were calculated according to the transfer function that uses absolute contrast between target and background surface. The blind condition that had 45 tilted angle toward ground provided good contrast and performance scores. Using a control algorithm of an automated daylight dimming control system lighting energy sayings were predicted. For all blind conditions minimum lighting energy was consumed.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.502-505
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• 2012

This study suggested 'Environmental Friendly City Model' and 'Energy Planning Process' according to the increasing necessity of 'Energy Saving Green City and 3 technologies like (1)Urban Spatial Modeling, (2)Urban Energy Consumption, (3)Urban Energy Supply Planning technologies were suggested which are able to support sustainable urban energy planning'. The results are as follows. (1)E-GIS modeling system was suggested as a 'Planning Supporting System'. (2)Urban Energy Consumption Algorithm was systemized with planning information of E-GIS DB. (3)Urban Energy System Location was deduced by integrating E-GIS DB and ANN algorithm.

• Journal of the Korean Solar Energy Society
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• v.28 no.1
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• pp.57-64
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• 2008

According to the Fourth Assessment Report of Intergovernmental Panel on Climate Change(IPCC) Working Group III, climate change is already in progress around the world, and it is necessary to execute mitigation in order to minimize adverse impacts. This paper suggests future climate change needs, employing IPCC Special Report on Emissions Scenarios(SRES) to predict temperature rises over the next 100 years. This information can be used to develop sustainable architecture applications for energy efficient buildings and renewable energy. Such climate changes could also affected the resent supplies of renewable energy sources. This paper discusses one recent Fourth Assessment Report of IPPC (Mitigation of Climate Change) and the Hadley Centre climate simulation of relevant data series for South Korea.