• 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)

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.312-326
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• 2020

Generation of weather forecasts at 100 m resolution through a statistical downscaling process was implemented by Korea Meteorological Administration Post- Processing (KMAPP) system. The KMAPP data started to be used in various industries such as hydrologic, agricultural, and renewable energy, sports, etc. Cheorwon area and Jeonbuk area have horizontal planes in a relatively wide range in Korea, where there are many complex mountainous areas. Cheorwon, which has a large number of in-situ and remotely sensed phenological data over large-scale rice paddy cultivation areas, is considered as an appropriate area for verifying KMAPP prediction performance in agricultural areas. In this study, the performance of predicting KMAPP temperature changes according to ecological changes in agricultural areas in Cheorwon was compared and verified using KMA and National Center for AgroMeteorology (NCAM) observations. Also, during the heat wave in Jeonbuk Province, solar radiation forecast was verified using Automated Synoptic Observing System (ASOS) data to review the usefulness of KMAPP forecast data as input data for application models such as livestock heat stress models. Although there is a limit to the need for more cases to be collected and selected, the improvement in post-harvest temperature forecasting performance in agricultural areas over ordinary residential areas has led to indirect guesses of the biophysical and phenological effects on forecasting accuracy. In the case of solar radiation prediction, it is expected that KMAPP data will be used in the application model as detailed regional forecast data, as it tends to be consistent with observed values, although errors are inevitable due to human activity in agricultural land and data unit conversion.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.579-587
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• 2016

To obtain a stable energy supply and manage PhotoVoltaic (PV) systems efficiently, satellite imagery methods are being developed to estimate the solar PV potential. This study analyzed trends in the use of satellite imagery in solar PV and solar irradiation estimation technology. The imaging technology is used to produce solar energy resource maps. The trend analysis showed that the level of solar PV technology in Korea is 30% below that of advanced countries. It is impossible to raise such low-level technologies to the levels of advanced countries quickly. Intensive research and development is the only way to achieve the 80% technology level of advanced countries. The information produced in this process can contribute to the management of solar power plants. A valid technology development strategy would be to obtain effective data that can be used for fieldwork. Such data can be produced by estimating solar irradiation very accurately with several-hundred-meter resolution using Communication, Ocean, and Meteorological Satellites (COMS) and next-generation GEO-KOMPSAT 2A, developing core technologies for short- and medium-term irradiation prediction, and developing technologies for estimating the solar PV potential.

• Journal of the Korean Solar Energy Society
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• v.30 no.2
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• pp.72-80
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• 2010

The main thrust of this paper is to investigate a practical way of generating the monthly averaged daily horizontal solar radiation in Korea. For estimating the horizontal solar radiation, the clearness index($K_T$) and the clearness number($C_N$) which are required for the use of Liu and Jordan's model and ASHRAE Clear Sky model were derived based on the measured weather data. Third-order polynomials returning $K_T$ and��$C_N$ for a given location were derived as a function of cloud amount, month, date, latitude and longitude. The predicted monthly averaged daily horizontal solar radiation values were compared with those acquired from the established design weather data. The MBE(Mean Bias Error) and RMSE (Root Mean Squares for Error) between the predicted values and the measured data were near zero. It means that the suggested third-order polynomials for $K_T$ and $C_N$ have good applicability to Liu and Jordan's model and ASHRAE Clear Sky model.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.348-350
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• 2003

본 논문에서는 태양광-풍력 복합발전시스템의 발전용량 예측을 통한 시스템 시설투자비 및 발전단가와 경제성에 대하여 분석한다. 도시지역의 일사량 및 풍속 데이터를 기초로 하여 복합발전시스템의 일일 발전량을 구하고, 수용가의 일일부하패턴과 수요부하를 고려하여 태양전지 어레이와 풍력발전기의 용량을 산정한다. 그리고 용량 산정에 따른 복합발전시스템의 초기투자비용과 연간 발전량, 연간 소요경비 등의 요소를 고려하여 총 수명가 분석법(Total Life-Cycle Cost Analysis)에 기초한 발전단가를 계산하고 잉여전력을 계통에 판매할 경우의 경제성을 평가한다.

• Journal of the Korean earth science society
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• v.23 no.5
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• pp.424-435
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• 2002

This study analyzes the surface ozone and meteorological data in Daegu for a period from 1997 to 1999. It also investigates the meteorological characteristics of high ozone episodes. For this study the high ozone episode has been defined as a daily maximum ozone concentration higher than 100ppb in at least one station among six air quality monitoring stations in Daegu, Korea. The frequency of episodes is 13 days. The frequency is the highest in May and September. The average value of daily maximum ozone concentration is 81.6ppb, and 8-hour average ozone concentration is 58.6ppb for the high episodes. This shows that ozone pollution is continuous and wide-ranging in Daegu. The daily maximum ozone concentration is positively correlated to solar radiation and daily maximum temperature, but negatively correlated to relative humidity, wind speed and cloud amount. The maximal correlation coefficient to solar radiation is 0.45. The differences between high ozone episode day's daily mean meteorological value and monthly mean value are +1.58hPa for sea level pressure, +3.45${\circ}$C for maximum temperature, -5.69% for relative humidity, -0.46ms$^{-1}$ for wind speed, -1.79 for cloud amount, and +3.97MJm$^{-2}$ for solar radiation, respectively. This shows that strong solar radiation, low wind speed and no precipitation between 0700${\sim}$1100LST are favorite conditions for high ozone episodes. It is related to the morning stagnant condition.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.374-374
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• 2021

최근 우리나라에도 대설로 인한 피해가 발생하고 있으며, 피해의 대부분은 강설 발생 이후 남아 있는 적설량이 주된 원인이 되고 있다. 적설량에 대한 예측은 대설피해에 대응하기 위한 중요한 정보이다. 따라서 본 연구에서는 융설량에 영향을 미칠것으로 판단되는 적설량, 기온, 습도, 일사량을 반영하여 일일 융설량을 모의하는 다중회귀모형을 구성하였다. 모형은 2000년부터 2020년까지의 강설 사상을 대상으로 구축하였으며, 2021년에 발생한 광주, 대관령, 목포, 서산, 전주 지역의 강설 사상에 적용하였다. 분석 대상 지역의 평균 적설량은 7.41 cm로 나타났으며, 평균 RMSE는 1.64 cm가 발생하였다. 오차의 원인으로는 적설량이 1 cm 미만 감소했을 경우, 바람이나 승화의 영향이 상대적으로 크게 작용할 수 있으나, 본 연구에 이용된 함수는 바람과 증발산 등이 고려되지 않았다. 또한, 회귀계수 결정에서 급격한 온도 변화를 능동적으로 반영하기 어려워 급상승한 온도나 매우 낮은 온도에 오차가 더 크게 나타난다. 따라서, 본 함수를 통하여 융설 깊이를 예측하기 위해서는 매우 높은 온도나, 매우 낮은 온도에서의 영향을 통제할 수 있는 변수 또는 상수를 추가할 필요가 있는 것으로 판단된다. 또한 초기 강설 당시의 기온과 습도 등에 따라, 눈의 결정이 달라지고, 이에 따라 융설에도 영향을 미칠 수 있다는 점을 이해하여, 초기 적설에 대한 변수도 고려되어야 할 것이다.