• Journal of Environmental Science International
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• v.27 no.6
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• pp.425-436
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• 2018

To establish initial response scenarios for nuclear accidents around the Kori nuclear power plants, the potential for radionuclide diffusion was estimated using numerical experiments and statistical techniques. This study used the numerical model WRF (Weather Research and Forecasting) and FLEXPART (Flexible Particle dispersion model) to calculate the three-dimensional wind field and radionuclide dispersion, respectively. The wind patterns observed at Gijang, near the plants, and at meteorological sites in Busan, were reproduced and applied to estimates of seasonally averaged wind fields. The distribution of emitted radionuclides are strongly associated with characteristics of topography and synoptic wind patterns over nuclear power plants. Since the terrain around the power plants is complex, estimates of radionuclide distribution often produce unexpected results when wind data from different sites are used in statistical calculations. It is highly probable that in the summer and autumn, radionuclides move south-west, towards the downtown metropolitan area. This study has clear limitations in that it uses the seasonal wind field rather than the daily wind field.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• Journal of the Korean Applied Science and Technology
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• v.32 no.1
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• pp.62-71
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• 2015

In order to deal with the globally increasing electric power demand and reduce $CO_2$ emission, complex thermoelectric power plants are being constructed in densely populated downtown areas. As the environmental regulations are continuously strengthened, various facilities like low NOx burner and SCR are being installed to reduce NOx emission. This study is applied using the TMS emission of $NO_2$ from combined heat and power plant located in Goyang-si Gyeonggi-do. Applying data to the computational fluid dynamics(CFD), and compared with the actual measurement results. It is judged that even though there might be differences between actual measurements and CFD results due to the instant changes of wind direction and wind speed according to measurement time during measurement period, modeling results and actual measurement results showed similar concentration at most forecasting areas and therefore, the forecasting concentration could be deducted which is close to actual measurement by calculating the contribution concentration considering the surrounding concentration in the future.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• Wind and Structures
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• v.20 no.6
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• pp.763-791
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• 2015

"Wind and Ports" is a European project that has been carried out since 2009 to handle wind forecast in port areas through an integrated system made up of an extensive in-situ wind monitoring network, the numerical simulation of wind fields, the statistical analysis of wind climate, and algorithms for medium-term (1-3 days) and short term (0.5-2 hours) wind forecasting. The in-situ wind monitoring network, currently made up of 22 ultrasonic anemometers, provides a unique opportunity for detecting high resolution thunderstorm records and studying their dominant characteristics relevant to wind engineering with special concern for wind actions on structures. In such a framework, the wind velocity of thunderstorms is firstly decomposed into the sum of a slowly-varying mean part plus a residual fluctuation dealt with as a non-stationary random process. The fluctuation, in turn, is expressed as the product of its slowly-varying standard deviation by a reduced turbulence component dealt with as a rapidly-varying stationary Gaussian random process with zero mean and unit standard deviation. The extraction of the mean part of the wind velocity is carried out through a moving average filter, and the effect of the moving average period on the statistical properties of the decomposed signals is evaluated. Among other aspects, special attention is given to the thunderstorm duration, the turbulence intensity, the power spectral density and the integral length scale. Some noteworthy wind velocity ratios that play a crucial role in the thunderstorm loading and response of structures are also analyzed.

• Wind and Structures
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• v.36 no.2
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• pp.121-131
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• 2023

Aiming at the problem that fatigue characteristics of metal roof rely on local physical tests and lacks the cyclic load sequence matching with regional climate, this paper proposed a method of constructing the fatigue load spectrum based on integration of wind load model, measured data of long-span metal roof and climate statistical data. According to the turbulence characteristics of wind, the wind load model is established from the aspects of turbulence intensity, power spectral density and wind pressure coefficient. Considering the influence of roof configuration on wind pressure distribution, the parameters are modified through fusing the measured data with least squares method to approximate the actual wind pressure load of the roof system. Furthermore, with regards to the wind climate characteristics of building location, Weibull model is adopted to analyze the regional meteorological data to obtain the probability density distribution of wind velocity used for calculating wind load, so as to establish the cyclic wind load sequence with the attributes of regional climate and building configuration. Finally, taking a workshop's metal roof as an example, the wind load spectrum is constructed according to this method, and the fatigue simulation and residual life prediction are implemented based on the experimental data. The forecasting result is lightly higher than the design standards, consistent with general principles of its conservative safety design scale, which shows that the presented method is validated for the fatigue characteristics study and health assessment of metal roof.

• Journal of Environmental Science International
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• v.19 no.2
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• pp.171-184
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• 2010

Accurate simulation of the meteorological field is very important to assess the wind resources. Some researchers showed that sea surface temperature (SST) plays a leading role on the local meterological simulation. New Generation Sea Surface Temperature (NGSST), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Real-Time Global Sea Surface Temperature (RTG SST) have different spatial distribution near the coast and OSTIA shows the best accuracy compared with buoy data in the southeastern coast of the Korean Peninsula. Those SST products are used to initialize the Weather Research and Forecasting (WRF) Model for November 13-23 2008. The simulation of OSTIA shows better result in comparison with NGSST and RTG SST. NGSST shows a large difference with OSTIA in horizontal and vertical wind fields during the weak synoptic condition, but wind power density shows a large difference during strong synoptic condition. RTG SST shows the similar patterns but smaller the magnitude and the extent.

• Wind and Structures
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• v.37 no.4
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• pp.315-329
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• 2023

We investigated the wind characteristics in the near-surface layer during the landfall of Typhoon Mujigae (2015) based on observations from wind towers in the coastal areas of Guandong province. Typhoon Mujigae made landfall in this region from 01:00 UTC to 10:00 UTC on October 4, 2015. In the region influenced by the eyewall of the tropical cyclone, the horizontal wind speed was characterized by a double peak, the wind direction changed by >180°, the vertical wind speed increased by three to four times, and the angle of attack increased significantly to a maximum of 7°, exceeding the recommended values in current design criteria. The vertical wind profile may not conform to a power law distribution in the near-surface layer in the region impacted by the eyewall and spiral rainband. The gust factors were relatively dispersed when the horizontal wind speed was small and tended to a smaller value and became more stable with an increase in the horizontal wind speed. The variation in the gust factors was the combined result of the height, wind direction, and circulation systems of the tropical cyclone. The turbulence intensity and the downwind turbulence energy spectrum both increased notably in the eyewall and spiral rainband and no longer satisfied the assumption of isotropy in the inertial subrange and the -5/3 law. This result was more significant in the eyewall area than in the spiral rainband. These results provide a reference for forecasting tropical cyclones, wind-resistant design, and hazard prevention in coastal areas of China to reduce the damage caused by high winds induced by tropical cyclones.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1085-1087
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• 2012

현재 급격한 화석 에너지의 사용 증가로 인해 자원이 고갈되고 있으며, 심각한 환경오염의 문제가 발생하고 있다. 이러한 화석 에너지의 문제점 때문에 무공해이면서 자원 량이 무한에 가까운 신재생 에너지가 거론되고 있는데, 그 중에서 경제적인 면과 기술력이 가장 발전한 풍력 에너지가 각광 받고 있다. 하지만 풍력 발전은 풍속이 짧은 시간 안에 급격한 변화를 일으켜 풍력 터빈의 손상을 초래하며 정확한 풍력발전량의 예측이 힘들어 전력 생산량이 불규칙하다. 그리하여 전력의 공급과 수요의 균형을 위해 풍력발전량의 정확한 예측이 필요하다. 따라서 이 연구에서는 ANFIS을 적용하고 전력 생산 변화의 빠르기 PRR을 이용하여 풍력발전량을 예측하였다. 실험에서는 ANFIS기법에 PRR속성을 이용하여 단순한 ANFIS 기법 보다 더 정확한 풍력 발전량의 예측 결과를 얻을 수 있었다.