• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.209-210
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• 2004

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1998.10a
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• pp.333-338
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• 1998

• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.95-96
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2002.10a
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• pp.245.1-245
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• 2002

• Proceedings of the Korean Nuclear Society Conference
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• 2003.10a
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• pp.155.2-155.2
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• 2003

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.96-103
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• 2024

The demand for mass calculation of offsite consequence analysis to conduct exhaustive single-unit or multi-unit Level 3 PSA is increasing. In order to perform efficient offsite consequence analyses, the Korea Atomic Energy Research Institute is conducting model optimization studies to minimize the analysis time while maintaining the accuracy of the results. A previous study developed a model optimization method using efficient plume segmentation and verified its effectiveness. In this study, we investigated the possibility of optimizing the model through particle size distribution setting by checking the reduction in analysis time and deviation of the results. Our findings indicate that particle size distribution setting affects the results, but its effect on analysis time is insignificant. Therefore, it is advantageous to set the particle size distribution as fine as possible. Furthermore, we evaluated the effect of multithreading and confirmed its efficiency. Future optimization studies should be conducted on various input factors of offsite consequence analysis, such as spatial grid settings.

• The Korean Journal of Food & Health Convergence
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• v.6 no.6
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• pp.17-23
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• 2020

The main task of agro-industrial production is to provide the population with food products for the production of which energy is expended in the form of electricity, technical means, fuels and lubricants, mineral fertilizers, etc. Accordingly, we have developed a concept and general methodological principles for the analysis of ecological and biotechnical systems in animal husbandry, it makes it possible to simulate the influence of various factors on the energy and ecological efficiency of systems, to compare and search for energy-saving modes and technologies. General methodological principles have been developed for the analysis of energy efficiency and environmental safety of agricultural ecological and biotechnical systems, which are based on the definition of the bioenergy efficiency coefficient, the quantitative expression of which is the ratio of energy accumulated in products to the total energy consumption for its production. This makes it possible to model with sufficient accuracy the influence of various factors on the energy and environmental efficiency of the system, to compare and search for energy-saving modes and technologies in order to find and select the most energy efficient ones to increase the energy efficiency of the complex.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1540-1547
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• 2013

The energy economic analysis of the standard rural house model with PV system was performed based on annual energy demand calculation using the EnergyPlus to contribute in reducing building energy which occupies 25% of national energy consumption and in developing a low-energy & eco-friendly house model. Two types of PV system installation was considered to cover electricity demand for cooling, electric, and heating devices. For the selected house model, heating energy demand is 7 times higher than cooling energy demand. For the Case1, it is favorable to use electricity from PV system for cooling and electric devices and to sell surplus electricity. For the Case2, it is favorable to use electricity from PV system for cooling, electricity and heating devices and to sell surplus electricity. Considering the installation cost of PV system and heat pump air conditioning system, the break-even point of Case1 and Case2 are about 13 and 11 years respectively. Although the installation cost of Case2 is more expensive, Case2 provides three times more profit than Case1 after the break-even point. Because the expected average life time of the selected PV system is 25 years, Case2 is more favorable option for the given standard rural house model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.2
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• pp.141-151
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• 2020

Since it was first studied in 1980, solar energy analysis model for geographic information systems has been used to determine the approximate spatial distribution of terrain. However, the spatial pattern was not able to be grasped in 3D (three-dimensional) space with low accuracy due to the limitation of input data. Because of computational efficiency, using a constant value for the brightness of the sky caused the simulation results to be less reliable especially when the slope is high or buildings are crowded around. For the above reasons, this study proposed a model that predicts solar energy of vertical surfaces of buildings with four stages below. Firstly, CIE (Commission Internationale de l'Eclairage) luminance distribution model was used to calculate the brightness distribution of the sky using NREL (National Renewable Energy Laboratory) solar tracking algorithm. Secondly, we suggested a method of calculating the shadow effect using ray tracing. Thirdly, LOD (Level of Detail) 3 of 3D spatial data was used as input data for analysis. Lastly, the accuracy was evaluated based on the atmospheric radiation data collected through the ground observation equipment in Daejeon, South Korea. As a result of evaluating the accuracy, NMBE was 5.14%, RMSE 11.12, and CVRMSE 7.09%.