• Journal of Energy Engineering
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• v.23 no.4
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• pp.56-60
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• 2014

Highly concentrated sunlight obtained from a solar concentrator mounted on a solar tracker can be divided into the infrared and visible region before it is actually applied. That is, solar rays are directed toward a unit optically separating sunlight into the infrared and visible region by a hot mirror as they impinge on the surface of a secondary reflector. The Infrared rays can be utilized for thermoacoustic applications while visible rays can be utilized for indoor lighting. This work introduces the separation of two different kinds of light; sunlight and artificial light. As for the artificial light, its wavelength extended from 400m to 720m for the visible region and 620m to 940m for the infrared region. Comparatively, a series of tests performed on sunlight revealed its separation in the visible region from 460m to 680m whereas from 620m to 940m for the artificial light.

• KIEAE Journal
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• v.3 no.1
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• pp.21-29
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• 2003

Scale model measurements should be conducted under an actual sky or in a simulated sky where conditions can be held constant. A number of successful attempts have been made to develop artificial sky domes with man-made sun emulators. With reference to formerly-developed examples, sky simulator facility has recently been activated in oder to provide desirable sky conditions for teaching and studies. The structure is a 6m-diameter dome and promises to set various condition for energy related and lighting research activities. The sky dome is also equipped a heliodon, the tilt table, to facilitate additive direct sun impact under clear skies. Shading studies, using scale models with the heliodon, reveal how a building's design blocks or permits light's passage to the interior; solar access studies, and tests of the reflection and transmittance characteristics of new daylighting technologies. The design and construction specification and the initial operating experience with a building configuration are reported.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.135-139
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• 2022

The current-voltage modeling plays an important role in characterizing photovoltaic systems. A solar cell has a nonlinear characteristic with various parameters influenced by the external environments such as the irradiance and the temperature. In order to accurately predict current-voltage characteristics at low irradiance, the artificial neural networks are applied to effectively quantify nonlinear behaviors. In this paper, a multi-layer perceptron scheme that can make accurate predictions is employed to learn complex formulas for large amounts of continuous data. The simulated results of artificial neural networks model show the accuracy improvement by using MATLAB/Simulink.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.316-321
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• 2000

In this study, heat collecting performance was study of flat plate solar collector by the angle. A method of study on were made turn out artificial sun by the angle of 0, 15, 30 degrees. The heat performances were measured the tube array surface temperature by thermo-couple. The winter season natural condition for 4 times on the angles of various general and emboss glass at optimum distance(0.68m) calculated of between sun and solar collector. To sum up temperature rise is appear more or less that emboss glass is all the better for general glass. The temperature variable at below of 30 degree was appear very less. The maximum performance of this system at that it is tilt angle of 30 with general glass is appear Q:11.54(kcal/min) and ${\Delta}T=18.9^{\circ}C$.

• Journal of Astronomy and Space Sciences
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• v.29 no.2
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• pp.97-101
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• 2012

Monitoring sunspots consistently is the most basic step required to study various aspects of solar activity. To achieve this goal, the observers must regularly calculate their own correction factor $k$ and keep it stable. Relatively recently, two observing teams in South Korea have presented interesting papers which claim that revisions that take the yearly-basis $k$ into account lead to a better agreement with the international relative sunspot number $R_i$, and that yearly $k$ apparently varies with the solar cycle. In this paper, using artificial data sets we have modeled the sunspot numbers as a superposition of random noise and a slowly varying background function, and attempted to investigate whether the variation in the correction factor is coupled with the solar cycle. Regardless of the statistical distributions of the random noise, we have found the correction factor increases as sunspot numbers increase, as claimed in the reports mentioned above. The degree of dependence of correction factor $k$ on the sunspot number is subject to the signal-to-noise ratio. Therefore, we conclude that apparent dependence of the value of the correction factor $k$ on the phase of the solar cycle is not due to a physical property, but a statistical property of the data.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.5-7
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• 2006

In this paper, we analyzed the elements of measurement uncertainty on electrical performance test which are the most important things in photovoltaic module performance test. Repeating the performance test by 6 men, the measurement uncertainty could be calculated. In this experiment, Solar Simulator (A-Class pulse type) used for domestic certificate test of PV module is Pasan IIIb (Balval, Switzerland). The possible elements of the measurement uncertain that could effect electrical performance test of PV module are reference cell, spectrum correction, error from measurement repetition, test condition, stability and uniformity of artificial solar simulator. To find the measurement uncertainty, 6 men repeated the test by 10 times. And the results were that numerical average value was 124.44W and measurement uncertainty was $124.44W{\pm}0.75W$ with 95% confidence level for 125W PV module.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Proceedings of the Korean Society of Potoscience Conference
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• spring
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• pp.22-22
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• 1996

• Journal of the Korean Solar Energy Society
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• v.32 no.spc3
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• pp.199-206
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• 2012

The purpose of this study is to understand the change of impression by comparing the uniformity lighting with the compound lighting. In previous study, we proposed a light controlling method to harmonize daylight from a window and artificial lights from a ceiling and obtained the results to support our method. We referred this method as the Adjusted Compound-Lighting Model (AC Model). The experiment is carried out with the scaled-models and mock-up spaces that were supposed to be an office space. One is lit by the uniform lighting and the other by the compound lighting in each experimental space. In order to present varying illuminance distributions, the two variables were used in this study. Subjects were asked to evaluate the point of difference by semantic differential rating on their overall impression after comparing with two rooms. The results showed that the impressions of compound lighting were more positive score than that of uniformity lighting on the items of 'dim-bright', 'dislike-like', 'artificial-natural' and 'closed-open', and that there was no significant difference in impressions between two spaces on other items.

• Solar Energy
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• v.11 no.2
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• pp.9-19
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• 1991

Lighting is one of the largest energy consumption in commercial building. For saving such lighting energy, integrated lighting system with daylight and artificial lighting has been suggested. In such system, perimeter zone can be illuminated by daylighting and the deep area of room by artificial lighting. So, the study aimed to develope of lighting energy prediction nomograph by turnning-off depth and lighting control systems during daytime. For the purpose, energy nomo-graph has been developed to apply to side-lit office building and the use and limitation of the nomograph has been discussed.