• Journal of Korean Society for Atmospheric Environment
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• v.15 no.5
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• pp.545-554
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• 1999

We would like to develop a short-term model to predict the time-related concentration of ozone whose reaction mechanism is complex. The paper targets Seoul where an ozone alert system has recently been employed. In order to develop a short-term prediction model for ozone, we suggested the Ozone Peak Indicator(OPI), an equivalent of the potential daily maximum ozone concentration, with precursors being the only limiting factor, and we calculated the Ozone Peak Indicarot as OPI={$rac{(O_3)_{max}cdot(H_{eH})_{max}(Rad)_{max}$ to preclude the influence of mixing height and solar radiation on the daily maximum ozone concentration. The OPI on the day of the prediction is to be calcultated by using the relation between OPI and the initial value of precursors. The basic prediction formula for time-related ozone concentration was established as $O_3(1)={(OPI)cdot Rad(t-2)H_{eH}}$, using the OPI, solar radiation two hours before prediction and mixing height. We developed, along with the basic formula for predicting photochemical oxidants, "SEOM"(Seoul Empirical Oxidants Model), a Fortran program that helps predict solar radiation and mixing height needed in the prediction of ozone pollution. When this model was applied to Seoul and an analysis of the correlation between the observed and the predicted ozone concentrations was made through SEOM, there appeared a very high correlation, with a coefficient of 0.815. SEOM can be described as a short-term prediction model for ozone concentration in large cities that takes into account the initial values of precursors, and changes in solar radiation and mixing height. SEOM can reflect the local characteristics of a particular and region can yield relatively good prediction results by a simple data input process.t process.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• 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 Korean Society of Steel Construction
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• v.9 no.3 s.32
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• pp.431-440
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• 1997

The paper describes a theoretical model for the prediction of bridge temperatures from meteorological data measured at bridge site and local meteorological center together with existing finite element heat transfer theory and solar radiation transfer theory to determine the time dependent temperature distribution of bridge. In this analytical model, the most adequate equation for the calculation of solar radiation on the bridge surface, which is dominant in day time is described based on the results of several experimental studies for the solar energy. The validity of this model is tested against field data obtained from long term experimental program on Sadang Viaduct in Seoul. Also, this paper describes the linear correlation between design variables and meteorological data to establish analytical criteria for the prediction of the average temperature, which are responsible for the longitudinal deformation of the bridges and of the vertical differential temperature profiles. which are responsible for the bending deformations from the long term experimental results.

• Atmosphere
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• v.27 no.4
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• pp.385-398
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• 2017

Numerical weather prediction experiments were carried out by applying topographic effects to reduce or enhance the solar radiation by terrain. In this study, x and ${\kappa}({\phi}_o,\;{\theta}_o)$ are precalculated for topographic effect on high resolution numerical weather prediction (NWP) with 1 km spatial resolution, and meteorological variables are analyzed through the numerical experiments. For the numerical simulations, cases were selected in winter (CASE 1) and summer (CASE 2). In the CASE 2, topographic effect was observed on the southward surface to enhance the solar energy reaching the surface, and enhance surface temperature and temperature at 2 m. Especially, the surface temperature is changed sensitively due to the change of the solar energy on the surface, but the change of the precipitation is difficult to match of topographic effect. As a result of the verification using Korea Meteorological Administration (KMA) Automated Weather System (AWS) data on Seoul metropolitan area, the topographic effect is very weak in the winter case. In the CASE 1, the improvement of accuracy was numerically confirmed by decreasing the bias and RMSE (Root mean square error) of temperature at 2 m, wind speed at 10 m and relative humidity. However, the accuracy of rainfall prediction (Threat score (TS), BIAS, equitable threat score (ETS)) with topographic effect is decreased compared to without topographic effect. It is analyzed that the topographic effect improves the solar radiation on surface and affect the enhancements of surface temperature, 2 meter temperature, wind speed, and PBL height.

• Current Photovoltaic Research
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• v.4 no.2
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• pp.80-86
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• 2016

Since the last five years it has become a lot of solar power plants installed. However, by installing the large-scale solar power station it is not easy to predict the actual generation years. Because there are a variety of factors, such as changes daily solar radiation, temperature and humidity. If the power output can be measured accurately it predicts profits also we can measure efficiency for solar power plants precisely. Therefore, Prediction of power generation is forecast to be a useful research field. In this study, out discovering the factors that can improve the accuracy of the prediction of the photovoltaic power generation presents the means to apply them to the power generation amount prediction.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.354-356
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• 2011

Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Solar energy is one of the most successful sources of renewable energy for the production of electrical energy following solar energy. And, the solar/photovoltaic cell generators depend on the solar radiation, which is a random variable so this poses difficulty in the system scheduling and energy dispatching, as the schedule of the photovoltaic cell generators availability is not known in advance. This paper proposes to use the two-layered artificial neural networks for predicting the actual solar radiation from the previous values of the same variable.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.56.1-56.1
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• 2011

The solar and meteorological resources map is calculated using by one-layer solar radiation model (GWNU model), satellites data and numerical model output on the Korean peninsula. The Meteorological input data to perform the GWNU model are retrieved aerosol optical thickness from MODIS (TERA/AQUA), total ozone amount from OMI (AURA), cloud fraction from geostationary satellites (MTSAT-1R) and temperature, pressure and total precipitable water from output of RDAPS (Regional Data Assimilation and Prediction System) and KLAPS (Korea Local Analysis and Prediction System) model operated by KMA (Korea Meteorological Administration). The model is carried out every hour using by the meteorological data (total ozone amount, aerosol optical thickness, temperature, pressure and cloud amount) and the basic data (surface albedo and DEM). And the result is analyzed the distribution in time and space and validated with 22 meteorological solar observations. The solar resources map is used to the solar energy-related industries and assessment of the potential resources for solar plant. The National Institute of Meteorological Research in KMA released $4km{\times}4km$ solar map in 2008 and updated solar map with $1km{\times}1km$ resolution and topological effect in 2010. The meteorological resources map homepage (http://www.greenmap.go.kr) is provided the various information and result for the meteorological-solar resources map.

• KIEAE Journal
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• v.12 no.6
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• pp.93-98
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• 2012

The concerns on high ozone concentration phenomenon is significantly growing in Seoul metropolitan area including the industry complex area, like Shiwha Banwol area. The aims of this research is the analysis of relationship between high concentrations of $O_3$ and solar radiation parameters in atmosphere. The understanding of the effects of solar radiation intensity, humidity, high air temperature on ozone concentration in a day is very useful to provide a direction for reducing of the high ozone concentration to a local government or a metropolitan government. The correlation analysis between maximum ozone concentration and various meteorological parameters in 2009 - 2011 carried out using IBM's SPSS program. The results showed that the mean correlations coefficient (R) between daily Ozone maximum and solar radiation resulted R = 0.64 during 2011. May - September in 10 air pollution stations. In case of correlations between daily ozone maximum and relative humidity showed negative correlation R = -0.61. The correlation analysis with mean air temperature during 1-3 PM resulted R = 0.29. This low correlation coefficient could be corrected by using of categorized data of ozone concentration. The daily maximum ozone concentration is more dependent on peak solar radiation and high air temperature during 1-3 PM than its simple daily maximum values. The results of this research would be used to develop the high ozone alert system around Seoul metropolitan area. This correlation analysis could be partially integrated to prediction of ozone peak concentration in connection with other methods like classification and regression tree(CART).

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.