• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.437-444
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• 2010

Soil moisture has been recognized as the essential parameter when understanding the complicated relationship between land surface and atmosphere in water and energy recycling system. It has been generally known that it is related with the temperature, wind, evaporation dependent on soil properties, transpiration due to vegetations and other constituents. There is, however, little research concerned about the relationship between soil moisture and these constitutes, thus it is needed to investigate it in detail. We estimated the soil moisture and then compared with field data using the hydrometerological data such as atmospheric temperature, specific humidity, and wind obtained from the Flux tower in Selmacheon, Korea. In the winter season, subterranean temperature showed highly positive correlation with soil moisture while it was negatively correlated from the spring to the fall. Estimation of seasonal soil moisture was compared with field measurements with the correlation of determination, R=0.82, 0.81, 0.82, and 0.96 for spring, summer, fall, and winter, respectively. Comprehensive relationship from this study can supply useful information about the downscaling of soil moisture with relatively large spatial resolutions, and will help to deepen the understanding of the water and energy recycling on the earth's surface.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.400-401
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• 2011

본 논문은 오피스 빌딩에서 최대 피크를 나타내는 여름철과 겨울철에 대한 부하사용량에 대해 신경회로망 알고리즘을 적용하여 일주일 단위를 예측하기 위한 단기예측 모델을 제시하였다. 2010년 7월~8월 사이의 최대전력사용량과 2010년 12월~2011년 1월 사이의 최대전력 사용량을 나타내는 시기에 온도, 습도, 풍속과의 연관성을 파악하기 위해 기후변화요소의 변수를 고려했을 때와 고려하지 않았을 때의 출력모델 비교를 통해 실제 전력사용 모델과 근접한 모델을 확인하였고 향후 최대부하 사용과 연관된 사용량 제어를 위한 알고리즘을 적용하여 전력사용량을 절약할 수 있는 방법을 시도하고자 한다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2007.10a
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• pp.185-191
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• 2007

최근 10년간 발생한 자연재해 중 태풍의 피해는 전체의 60%를 넘을 정도로 풍수해의 피해는 막대하며, 지속적 산업화와 개발로 인해 피해 규모 역시 매년1조원 이상을 상회하고 있어, 자연재해에 대한 피해 경감 노력이 매우 요구되고 있다. 이를 위하여 최근 풍수해의 피해 사전에 예측함으로써 예방 및 대비는 물론 재해 발생에 따른 응급 대응 및 복구의 효율성을 제고하고자하는 과학적 방법론에 대한 연구가 진행되고 있다. 태풍에 의한 피해 예측은 위험도(Hazard)의 추정, 피해 대상 자료의 구축(Inventory) 및 피해대상의 취약도(Fragility)의 세 가지 요소를 이용하여 수행되는 것이 일반적이다. 위험도는 자연재해의 특성인 강우, 풍속 등을 물리적으로 모델링함으로써 추정할 수 있으며, 피해 대상 자료는 공공 및 사유 시설물을 총 망라함으로써 피해의 사회, 경제적인 피해 규모 예측에 활용된다. 각각의 피해 대상이 위험도에 따라 갖는 취약도는 최종 피해 및 손실 규모의 평가 자료로 이용된다. 이때 위험도의 추정 및 피해 대상 자료의 구축을 위한 핵심적인 방법론으로서 지리정보시스템의 활용이 크게 요구된다. 따라서 본 연구에서는 태풍 피해 예측을 위한 자연재해 위험성 평가 방법론에 있어서 매우 중요한 요소인 자연 지형, 지표의 특성 및 활용도, 피해 대상인 인공 시설물 등의 자료항목을 분류하고 태풍 피해 예측 기술의 핵심 요소로서의 지리정보시스템 활용 방안을 제시하고자 한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.319-328
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• 2021

In this paper, we perform prediction of amount of electric power plant for complex of wind plant using multi-layer perceptron in order to calculate exact calculation of capacity of ESS to maximize profit through generation and to minimize generation cost of wind generation. We acquire wind speed, direction of wind and air density as variables to predict the amount of generation of wind power. Then, we merge and normalize there variables. To train model, we divide merged variables into data as train and test data with ratio of 70% versus 30%. Then we train model by using training data, and we alsouate the prediction performance of model by using test data. Finally, we present the result of prediction in amount of wind power.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.11-17
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• 2019

In recent years, damage and loss of life and property have been occurred frequently due to swelling waves in the East Sea. Swelling waves are not easy to predict because they are caused by various factors. In this research, we build a model for predicting the swelling waves occurrence in the East Coast of Korea using machine learning technique. We collect historical data of unloading interruption in the Pohang Port, and collect air pressure, wind speed, direction, water temperature data of the offshore Pohang Port. We select important variables for prediction, and test various machine learning prediction algorithms. As a result, tide level, water temperature, and air pressure were selected, and Random Forest model produced best performance. We confirm that Random Forest model shows best performance and it produces 88.86% of accuracy

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Journal of the Microelectronics and Packaging Society
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• v.26 no.4
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• pp.75-82
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• 2019

Flexible solar cells have attracted enormous attention in recent years due to their wide applications such as portable batteries, wearable devices, robotics, drones, and airplanes. In particular, the demands of the flexible silicon and compound semiconductor solar cells with high efficiency and high reliability keep increasing. In this study, we fabricated a flexible InGaP/GaAs double-junction solar module. Then, the effects of the wind speed and ambient temperature on the operating temperature of the solar cell were analyzed with the numerical simulation. The temperature distributions of the solar modules were analyzed for three different wind speeds of 0 m/s, 2.5 m/s, and 5 m/s, and two different ambient temperature conditions of 25℃ and 33℃. The flexibility of the flexible solar module was also evaluated with the bending tests and numerical bending simulation. When the wind speed was 0 m/s at 25 ℃, the maximum temperature of the solar cell was reached to be 149.7℃. When the wind speed was increased to 2.5 m/s, the temperature of the solar cell was reduced to 66.2℃. In case of the wind speed of 5 m/s, the temperature of the solar cell dropped sharply to 48.3℃. Ambient temperature also influenced the operating temperature of the solar cell. When the ambient temperature increased to 33℃ at 2.5 m/s, the temperature of the solar cell slightly increased to 74.2℃ indicating that the most important parameter affecting the temperature of the solar cell was heat dissipation due to wind speed. Since the maximum temperatures of the solar cell are lower than the glass transition temperatures of the materials used, the chances of thermal deformation and degradation of the module will be very low. The flexible solar module can be bent to a bending radius of 7 mm showing relatively good bending capability. Neutral plane analysis was also indicated that the flexibility of the solar module can be further improved by locating the solar cell in the neutral plane.

• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.273-276
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• 2006

신재생에너지의 효율 향상을 위한 시스템 개발과 병행하여 검토되어야 할 부문이 경제성 분석이다. 본 논문은 제주도 전력계통에 연계하여 운영중인 풍력발전의 자원과 발전량을 모의하기 위하여 제주도의 전력수요와 발전설비 특성, HDVC 수전 데이터, 풍속 등의 자료를 기반으로 가격예측을 위한 범용 소프트웨어들을 사용하여 에너지 시장 측면에서 풍력발전이 갖는 경제적 가치를 분석하였다.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.11a
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• pp.170-172
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• 2000

산업의 발전과 기상 변화에 따른 대기중의 오존 농도 메커니즘은 질소산화물 및 탄화 수소류 등의 오염 물질로 인한 광화학적인 작용과 일사량, 풍속, 기온 등의 기상학적인 변수들의 상호작용으로 생성되어 최근 국내외를 막론하고 하계 중 6월부터 8월 사이에 집중적인 고농도 현상을 보이는 것에 관심을 가지고 있다. (중략)

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2003.11a
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• pp.131-132
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• 2003

확산 모델의 선정은 도시, 임해 산악 지역, 임해 평야지역, 계곡지역, 분지지역 등 각 지형에 대해 계절별, 오염물질별, 평균화시간별로 적절성이 있어 보이는 여러 경쟁모델들을 적용해 보고 그 중에서 실측결과에 가장 근접하는 계산치를 제공하는 모델을 선정해야 한다. 특히 Dioxin등 독성물질들에 대해서는 대부분의 해석학적 확산모델들을 적용하기 곤란한 저풍속, 풍향요동(Meandering) 조건시 등에도 단시간 평균농도 계산치의 정확도가 높은 모델들을 선정하는 것이 중요하다(박옥현 등, 1999). (중략)