• 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.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• 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 earth science society
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• v.30 no.1
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• pp.58-68
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• 2009

Three-dimensional distributions of longwave radiation flux for the April-September 1998 period are generated from radiative transfer calculations using the GEWEX Asian Monsoon Experiment (GAME) reanalysis temperature and humidity profiles and International Satellite Cloud Climatology Project (ISCCP) cloudiness as inputs to understand the effect of cloud radiative forcing in the monsoon season. By subtracting the heating of the clear atmosphere from the cloudy radiative heating, cloud-induced atmospheric radiative heating has been obtained. Emphasis is placed on the impact of horizontal gradients of the cloud-generated radiative heating on the Asian monsoon. Cloud-induced heating exhibits its maximum heating areas within the Indian Ocean and minimum heating over the Tibetan Plateau, which establishes the north-south oriented differential heating gradient. Considering that the differential heating is a ultimate source generating the atmospheric circulation, the cloud-induced heating gradient established between the Indian Ocean and the Plateau can enhance the strength of the north-south Hadley-type monsoon circulation. Cooling at cloud top and warming at cloud bottom, which are the vertical distributions of cloud-induced heating, can exert on the monsoon circulation by altering the atmospheric stability.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.233-239
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• 2018

Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• Food Science and Preservation
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• v.19 no.5
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• pp.652-658
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• 2012

This study, the changes in physicochemical properties and hesperidin content of Jeju-processed citrus fruits according to the harvest date were evaluate. The soluble-solid content, pH, and soluble solid-acid ratios gradually increased, but titratable acidity slightly decreased with a delay in the harvest date. The color index, lightness, yellowness, and turbidity slightly decreased whereas the redness slightly increased with a delay in the harvest date. The hesperidin content slightly decreased with a delay in the harvest date. Hesperidin, which is the major cause of juice cloudiness, decreased with a delay in the harvest date. These results suggest that later-harvested fruit juice is bound to be less cloudy.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.14 no.2
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• pp.207-223
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• 2001

The Wonyenaejang is equivalent to the (senile)cataract in western medicine. The word cataract is used to describe the natural lens that has turned cloudy. As the natural lens of the eye becomes cloudy, it does not allow light to pass through it. Cataracts usually start as a slight cloudiness that progressively grows more opaque. As the cataract becomes more mature(increasingly opaque and dense), the retina receives less and less light. The light that does reach the retina becomes increasingly blurred and distorted. This causes gradual impairment of vision. If left untreated, cataracts can cause needless blindness. Although there are many kinds of cataracts, a senile cataract is the most common one. We chose the oriental medicine textbooks and the oriental medicine journals that were dealing with the symptoms, etiology, and internal/external treatments. The results were as follows : 1. The main causes of this disease are weak liver and kidney, burning up of the wind and heat in the liver and gall, weak spleen and stomach. 2. As the internal treatment of the Cataract, Geegukjihwangtang is mostly prescribed. 3. As the external treatment of the Cataract, (l) In the field of medicine for external application is commonly prescribed (2) In the field of drug action, frequently used treatments are as follows. emission of the evil, alleviation of fever, removal of lump of blood, and the medicine for external applications. (3) In the field of four Qi, cold medicine is commonly prescribed. (4) In the field of five tastes, bitter/hot/sweet mdicine are commonly prescribed. (5) In the field of toxicity, non-togic medicine is commonly prescribed. (6) In the field of channel distribution, most of the medicine belong to liver channel.

• Atmosphere
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• v.32 no.4
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• pp.277-295
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• 2022

The global weather prediction model, Korean Integrated Model (KIM), has been in operation since April 2020 by the Korea Meteorological Administration. This study assessed the performance of heat waves (HWs) in Korea in 2020. Case experiments during 2018-2020 were conducted to support the reliability of assessment, and the factors which affect predictability of the HWs were analyzed. Simulated expansion and retreat of the Tibetan High and North Pacific High during the 2020 HW had a good agreement with the analysis. However, the model showed significant cold biases in the maximum surface temperature. It was found that the temperature bias was highly related to underestimation of downward shortwave radiation at surface, which was linked to cloudiness. KIM tended to overestimate nighttime clouds that delayed the dissipation of cloud in the morning, which affected the shortage of downward solar radiation. The vertical profiles of temperature and moisture showed that cold bias and trapped moisture in the lower atmosphere produce favorable conditions for cloud formation over the Yellow Sea, which affected overestimation of cloud in downwind land. Sensitivity test was performed to reduce model bias, which was done by modulating moisture mixing parameter in the boundary layer scheme. Results indicated that the daytime temperature errors were reduced by increase in surface solar irradiance with enhanced cloud dissipation. This study suggested that not only the synoptic features but also the accuracy of low-level temperature and moisture condition played an important role in predicting the maximum temperature during the HWs in medium-range forecasts.

• Journal of the Korean Geographical Society
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• v.30 no.3
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• pp.207-229
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• 1995

The characteristics of weather and climate in South Korea has great influences on the annual variation pattern and the appearance of the prevailing weather. The purpose of this paper is to induce the quantity of the weather entropy and annual variation pattern using the information theory and the principal component analysis. And author tried to classify the region according to the variation of its space scale, The raw materials used for this study are the daily cloudiness and precipitation during the years 1990-1994 at 69 stations in South Korea. It is divided into four classes of fine, clear, cloudy and rainy. The rcsults of this study can be summarized as follows: 1. Thc characteristics of annual variation pattern of weather entropy can be chiefly divided into five categories and the accumulated contributory rate of these is 73.1%. 2. Annual variation pattern of the first principal component reaches smaller in May, April and September than national average, and becomes greater when the winter comes. This weather entropy's quantity(Rs1) is positive in most area to the western sife of Soback Mountains and negative in most seaside area to the eastern side of Soback Mountains. 3. The characteristics of annual variation pattern of the second principal component shows that the entropy is more smaller in summer than national average and the rest of seasons shows larger, especially in January, May and September. This weather entropy's quantity(Rs2) is positive in most Honam Inland area to the western side of Soback Mountains and negative in most Youngnam Inland area to the eastern side of Soback Mountains. 4. Eight type regions (S1-S11) are classified based on the occurrences of minimum weather entropy in South Korea, and annual variation pattern of weather entropy by principal component analysis may be classified into sixteen type regions (Rs1-Rs9). Putting these things together, South Korea can be classifieed into thirty one type regions (Rs1S7-Rs9S10).