• Journal of the Korean Solar Energy Society
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• v.31 no.2
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• pp.37-46
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• 2011

The GWNU (Gangnung-Wonju national university) solar radiation model was developed with radiative transfer theory by Iqbal and it is applied the NREL (National Research Energy Laboratory). Input data were collected and accomplished from the model prediction data from RDAPS (Regional Data Assimilated Prediction Model), satellite data and ground observations. And GWNU solar model calculates not only horizontal surface but also complicated terrain surface. Also, We collected the statistical data related on photovoltaic power generation of the Korean Peninsula and analyzed about photovoltaic power efficiency of the Gangwon region. Finally, the solar energy resource and photovoltaic generation possibility map established up with 4 km, 1 km and 180 m resolution on Gangwon region based on actual equipment from Shinan solar plant,statistical data for photovoltaic and complicated topographical effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.514-517
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• 2017

In solar photovoltaic systems, power generation is greatly affected by the weather conditions, so it is essential to predict solar energy for stable load operation. Therefore, data on weather conditions are needed as inputs to machine learning algorithms for solar energy prediction. In this paper, we use 15 kinds of weather data such as the precipitation accumulated during the 3 hours of the surface, upward and downward longwave radiation average, upward and downward shortwave radiation average, the temperature during the past 3 hours at 2 m above from the ground and temperature from the ground surface as input data to the algorithm. We analyzed the statistical characteristics and correlations of weather data and extracted the downward and upward shortwave radiation averages as a major elements of a feature vector with high correlation of 70% or more with solar energy.

• Journal of the Korean Solar Energy Society
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• v.31 no.5
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• pp.27-32
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• 2011

Most of domestic building generally don't have fixed shading devices considering of appearance and aesthetic issues. In this study is suggested that tilt window simultaneously has a role of shading and blocking solar radiation. The tilt window thermal performance is investigated by relation ship between inclination and heating cooling road. As comparing vertical window with $5^{\circ}$ and $7^{\circ}$ of tilt window respectively, the heating load is increased by 3.6% and cooling load is reduced by 8.1% on $5^{\circ}$ tilt window and the heating load is increased by 5.3% and cooling load is reduced by 11.5% on $5^{\circ}$ tilt window. Especially, the total load of alternative tilt window is showed the reduction rate 2.6% and3.6% compared of vertical window. Therefore, the tilt window is possible to role of shading of solar radiation and reduction of heating and cooling load.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.794-799
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• 2001

The simulation analyses of a solar power system with monolithic concentrator by using a stirling engine are carried out to predict the system performance in four test sites. The site has different intensities and distributions of direct solar radiation respectively. Seoul, Pusan and Cheju in Korea, and Naha in Japan are selected as test sites. To accomplish the same demand of a 25 kW output that the power level of a system has, it needs to take the matching of collector/receiver with engine/generator systems. In such a case, also, the size of the collector is sometimes adjusted. In this study, the diameter of the collector is decided by using the solar radiation of design point, which is defined as the sum of average and standard deviation $\sigma$ of maximum direct solar radiation distribution for a day during a year in the respective test site. It is found that the average power output during the system operating time in the case of slope error ${\sigma}_s=2.5$ is within the range of 9 to 13 kW.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.156-172
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• 2020

The purpose of this study was to predict road surface temperature using high-resolution solar radiation data. The road surface temperature prediction model (RSTPM) was applied to predict road surface temperature; this model was developed based on the heat-balance method. In addition, using SOLWEIG (SOlar and LongWave Environmental Irradiance Geometry-model), the shadow patterns caused by the terrain effects were analyzed, and high-resolution solar radiation data with 10 m spatial resolution were calculated. To increase the accuracy of the shadow patterns and solar radiation, the day that was modeled had minimal effects from fog, clouds, and precipitation. As a result, shadow areas lasted for a long time at the entrance and exit of a tunnel, and in a high-altitude area. Furthermore, solar radiation clearly decreased in areas affected by shadows, which was reflected in the predicted road surface temperatures. It was confirmed that the road surface temperature should be high at topographically open points and relatively low at higher altitude points. The results of this study could be used to forecast the freezing of sections of road surfaces in winter, and to inform decision making by road managers and drivers.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.33.3-34
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• 2008

As an activity of building Korean Space Weather Prediction Center (KSWPC), we has studied of radiation effect on the spacecraft components. High energy charged particles trapped by geomagnetic field in the region named Van Allen Belt can move to low altitude along magnetic field and threaten even low altitude spacecraft. Space Radiation can cause equipment failures and on occasions can even destroy operations of satellites in orbit. Sun sensors aboard Science and Technology Satellite (STSAT-1) was designed to detect sun light with silicon solar cells which performance was degraded during satellite operation. In this study, we try to identify which particle contribute to the solar cell degradation with ground based radiation facilities. We measured the short circuit current after bombarding electrons and protons on the solar cells same as STSAT-1 sun sensors. Also we estimated particle flux on the STSAT-1 orbit with analyzing NOAA POES particle data. Our result clearly shows STSAT-1 solar cell degradation was caused by energetic protons which energy is about 700 keV to 1.5 MeV. Our result can be applied to estimate solar cell conditions of other satellites.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.643-650
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• 2017

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• Journal of Ocean Engineering and Technology
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• v.15 no.4
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• pp.53-59
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• 2001

The Primary Production of phytoplanktons produces organic matter in high concentration in eutrophicated Hakata Bay, Japan, even during the winter season in spite of low water temperature. Phytoplanktons are considered to have any biological capabilities to keep activities of photosynthesis under the unfavorable conditions, and this affects water quality of the bay. In this study, seasonal variations in primary production efficiency were predicted by using a simple box-type ecosystem model, which introduced the concept of efficiency for absorption of solar radiation energy in relation to growth of phytoplanktons under the low solar radiation intensity. According to the simulation result of primary production, it was organic pollution comes from dissolved organic carbon (DOC) throughout the year, DOC of which is originated from the primary production of phytoplanktons on biological response of the seasonal variation of ambient conditions.