• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.41-53
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• 2014

The special observation using Radiosonde was performed to investigate precipitation events over the east coast of Korea during the winter season from 5 January to 29 February 2012. This analysis focused on the various indices to describe the characteristics of the atmospheric instability. Equivalent Potential Temperature (EPT) from surface (1000 hPa) to middle level (near 750 hPa) was increased when the precipitation occurred and these levels (1000~750 hPa) had moisture enough to cause the instability of atmosphere. The temporal evolution of Convective Available Potential Energy (CAPE) appeared to be enhanced when the precipitation fell. Similar behavior was also observed for the temporal evolution of Storm Relative Helicity (SRH), indicating that it had a higher value during the precipitation events. To understand a detailed structure of atmospheric condition for the formation of precipitation, the surface remote sensing data and Automatic Weather System (AWS) data were analyzed. We calculated the Total Precipitable Water FLUX (TPWFLUX) using TPW and wind vector. TPWFLUX and precipitation amount showed a statistically significant relationship in the north easterly winds. The result suggested that understanding of the dynamical processes such as wind direction be important to comprehend precipitation phenomenon in the east coast of Korea.

• Atmosphere
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• v.16 no.4
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• pp.359-370
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• 2006

Terrain height variance spectra for the Korean mountain region are calculated in order to determine an adequate grid size required to resolve terrain forcing on mesoscale model simulation. One-dimensional spectral analysis is applied to specifically the central-eastern part of the Korean mountain region, where topographical-scale forcing has an important effect on mesoscale atmospheric flow. It is found that the terrain height variance spectra in this mountain region has a wavelength dependence with the power law exponents of 1.5 at the wavelength near 30 km, but this dependence is steeply changed to 2.5 at the wavelength less than 30 km. For the adequate horizontal grid size selection on mesoscale simulation two-dimensional terrain height spectral analysis is also performed. There is no directionality within 50% of spectral energy region, so one-dimensional spectral analysis can be reasonably applied to the Korea Peninsula. According to the spectral analysis of terrain height variance, the finer grid size which is higher than 6 km is required to resolve a 90% of terrain variance in this region. Numerical simulation using WRF (Weather Research and Forecasting Model) was performed to evaluate the effect of different terrain resolution in accordance with the result of spectral analysis. The simulated results were quantitatively compared to observations and there was a significant improvement in the wind prediction across the mountain region as the grid space decreased from 18 km to 2 km. The results will provide useful guidance of grid size selection on mesoscale topographical simulation over the Korean mountain region.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.653-660
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• 2021

Renewable energy resources are rapidly becoming an integral part of electricity generation portfolios around the world due to declining costs, government subsidies, and corporate sustainability goal. Interacting wind, solar, and load forecast errors can create significant unpredictable impacts on the distribution system, feeder congestion, voltage standard and reactive power stability margins. These impacts will be increasing with the increasing penetration levels of variable renewable generation in the power systems. There is a limit to the maximum amount of renewable energy sources that can be connected in a distribution feeder by the connection rule of transmission & distribution facility in Korea. This study represents the decision plans of hosting capacity for distribution feeders without the need for significant upgrades to the existing transmission infrastructure. Especially, the paper suggests and discusses the hosting capacity standard of feeder cables and minimum load calculation of distribution feeders.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Journal of the Korean Society for Marine Environment & Energy
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• v.8 no.1
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• pp.46-52
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• 2005

A marine environmental information system (MEIS) useful for optimal route planning of ships running in the ocean was developed. Utilizing the simulated marine environmental data produced by the European Center for Medium-Range Weather Forecasts based on global environmental data observed by satellites, the real-time forecast and long-term statistics of marine environments around planned and probable ship routes are provided. The MEIS consists of a land-based data acquisition and analysis system(MEIS-Center) and a onboard information display system(MEIS-Ship) for graphic description of marine information and optimal route planning of ships. Also, it uses of satellite communication system for data transfer. The marine environmental components of winds, waves, air pressures and storms are provided, in which winds are described by speed and direction and waves are expressed in terms of height, direction and period for both of wind waves and swells. The real-time information is characterized by 0.5° resolution, 10 day forecast in 6 hour interval and daily update. The statistic information of monthly average and maximum value expected for a return period is featured by 1.5° resolution and based on 15 year database. The MEIS-Ship include an editing tool for route simulation and the forecasting and statistic information on planned routes can be displayed in graph or table. The MEIS enables for navigators to design an optimal navigational route that minimizes probable risk and operational cost.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.572-583
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• 2019

In this study, the atmospheric vertical structure (AVS) associated with summertime (June, July, and August) heavy rainfall in Seoul was classified into three patterns (Loaded Gun: L, Inverted V: IV, and Thin Tube: TT) using rawinsonde soundings launched at Osan from 2009 to 2018. The characteristics of classified AVS and precipitation property were analyzed. Occurrence frequencies in each type were 34.7% (TT-type), 20.4% (IV-type), 20.4% (LG-type), and 24.5% (Other-type), respectively. The mean value of Convective Available Potential Energy (1131.1 J kg^-1) for LG-types and Storm Relative Helicity (357.6 ㎡s^-2) for TT-types was about 2 times higher than that of other types, which seems to be the difference in the mechanism of convection at the low level atmosphere. The composited synoptic fields in all cases showed a pattern that warm and humid southwesterly wind flows into the Korean Peninsula. In the cases of TT-type, the low pressure center (at 850 hPa) was followed by the trough in upper-level (at 500 hPa) as the typical pattern of a low pressure deepening. The TT-type was strongly influenced by the low level jet (at 850 hPa), showing a pattern of connecting the upper- and low-level jets. The result of analysis indicated that precipitation was intensified in the first half of all types. IV-type precipitation induced by thermal instability tended to last for a short term period with strong precipitation intensity, while TT-type by mechanical instability showed weak precipitation over a long term period.

• Atmosphere
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• v.23 no.1
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• pp.1-11
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• 2013

The angular momentum transport of an idealized axisymmetric vortex in the developing stage was investigated using the Weather Research and Forecast (WRF) model. The balanced axisymmetric vortex was constructed based on an empirical function for tangential wind, and the temperature, geopotential, and surface pressure were obtained from the balanced equation. The numerical simulation was carried out for 6 days on the f-plane with the Sea Surface Temperature (SST) set as constant. The weak vortex at initial time was intensified with time, and reached the strength of tropical cyclone in a couple of days. The Absolute Angular Momentum (AAM) was transported along with the secondary circulation of the vortex. Total AAM integrated over a cylinder of radius of 2000 km decreased with simulation time, but total kinetic energy increased rapidly. From the budget analysis, it was found that the surface friction is mainly responsible for the decrease of total AAM. Also, contribution of the surface friction to the AAM loss was about 90% while that of horizontal advection was as small as 8%. The trajectory of neutral numerical tracers following the secondary circulation was presented for the Lagrangian viewpoint of the transports of absolute angular momentum. From the analysis using the trajectory of tracers it was found that the air parcel was under the influence of the surface friction continuously until it leaves the boundary layer near the core. Then the air parcel with reduced amount of angular momentum compared to its original amount was transported from boundary layer to upper level of the vortex and contributed to form the anti-cyclone. These results suggest that the tropical cyclone loses angular momentum as it develops, which is due to the dissipation of angular momentum by the surface friction.