• Wind and Structures
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• v.38 no.6
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• pp.461-475
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• 2024

A reliable wind speed forecasting method is crucial for the applications in wind engineering. In this study, the generalized S-transform (GST) is innovatively applied for wind speed forecasting to uncover the time-frequency characteristics in the non-stationary wind speed data. The improved grey wolf optimizer (IGWO) is employed to optimize the adjustable parameters of GST to obtain the best time-frequency resolution. Then a hybrid method based on IGWO-optimized GST is proposed to validate the effectiveness and superiority for multi-step non-stationary wind speed forecasting. The historical wind speed is chosen as the first input feature, while the dynamic time-frequency characteristics obtained by IGWO-optimized GST are chosen as the second input feature. Comparative experiment with six competitors is conducted to demonstrate the best performance of the proposed method in terms of prediction accuracy and stability. The superiority of the GST compared to other time-frequency analysis methods is also discussed by another experiment. It can be concluded that the introduction of IGWO-optimized GST can deeply exploit the time-frequency characteristics and effectively improving the prediction accuracy.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.244-252
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• 2021

Wind plays an important role in cases of unexpected radioactive pollutant dispersion, deciding distribution and concentration of the leaked substance. The accurate prediction of wind has been challenging in numerical weather prediction models, especially near the surface because of the complex interaction between turbulent flow and topographic effect. In this study, we investigated the characteristics of atmospheric dispersion of radioactive material (i.e. ¹³⁷Cs) according to the simulated boundary layer around the HANARO research nuclear reactor in Korea using the Weather Research and Forecasting (WRF)-Mesoscale Model Interface (MMIF)-California Puff (CALPUFF) model system. We examined the impacts of orographic drag on wind field, stability calculation methods, and planetary boundary layer parameterizations on the dispersion of radioactive material under a radioactive leaking scenario. We found that inclusion of the orographic drag effect in the WRF model improved the wind prediction most significantly over the complex terrain area, leading the model system to estimate the radioactive concentration near the reactor more conservatively. We also emphasized the importance of the stability calculation method and employing the skillful boundary layer parameterization to ensure more accurate low atmospheric conditions, in order to simulate more feasible spatial distribution of the radioactive dispersion in leaking scenarios.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.2
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• pp.115-125
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• 2024

Wind is a meteorological factor that has a significant impact on agriculture. Gust cause damage such as fruit drop and damage to facilities. In this study, low-altitude wind speed prediction was performed by applying physical models to Local Ensemble Prediction System (LENS). Logarithmic Law (LOG) and Power Law (POW) were used as the physical models, and Korea Ministry of Environment indicators and Moderate Resolution Imaging Spectroradiometer (MODIS) data were applied as indicator variables. We collected and verified wind and gust data at 3m altitude in 2022 operated by the Rural Development Administration, and presented the results in scatter plot, correlation coefficient, Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Threat Score (TS). The LOG-applied model showed better results in wind speed, and the POW-applied model showed better results in gust.

• New & Renewable Energy
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• v.4 no.4
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.1085-1092
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• 2017

In this paper, we propose a wind forecasting method that reflects wind characteristics to improve the accuracy of wind power prediction. The proposed method consists of extracting wind characteristics and predicting power generation. The part that extracts the characteristics of the wind uses correlation analysis of power generation amount, wind direction and wind speed. Based on the correlation between the wind direction and the wind speed, the feature vector is extracted by clustering using the K-means method. In the prediction part, machine learning is performed using the SVR that generalizes the SVM so that an arbitrary real value can be predicted. Machine learning was compared with the proposed method which reflects the characteristics of wind and the conventional method which does not reflect wind characteristics. To verify the accuracy and feasibility of the proposed method, we used the data collected from three different locations of Jeju Island wind farm. Experimental results show that the error of the proposed method is better than that of general wind power generation.

• Journal of the Korean Solar Energy Society
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• v.27 no.4
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• pp.1-9
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• 2007

Sensitivity analysis of wind resource micrositing has been performed through the application case at the Antarctic King Sejong station with the most representative micrositing softwares: WAsP, WindSim and Meteodyn WT. The wind data obtained from two met-masts separated 625m were applied as a climatology input condition of micro-scale wind mapping. A tower shading effect on the met-mast installed 20m apart from the warehouse has been assessed by the CFD software Fluent and confirmed a negligible influence on wind speed measurement. Theoretically, micro-scale wind maps generated by the two met-data located within the same wind system and strongly correlated meteor-statistically should be identical if nothing influenced on wind prediction but orography. They, however, show discrepancies due to nonlinear effects induced by surrounding complex terrain. From the comparison of sensitivity analysis, Meteodyn WT employing 1-equation turbulence model showed 68% higher RMSE error of wind speed prediction than that of WindSim using the ${\kappa}-{\epsilon}$ turbulence model, while a linear-theoretical model WAsP showed 21% higher error. Consequently, the CFD model WindSim would predict wind field over complex terrain more reliable and less sensitive to climatology input data than other micrositing models. The auto-validation method proposed in this paper and the evaluation result of the micrositing softwares would be anticipated a good reference of wind resource assessments in complex terrain.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.59.2-59.2
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• 2011

Although there are many activities on the construction of wind farm to produce amount of power from the wind, in practice power productions are not as much as its expected capabilities. This is because a lack of both the prediction of wind resources and the aerodynamic analysis on turbines with far wake effects. In far wake region, there are velocity deficits and increases of the turbulence intensity which lead to the power losses of the next turbine and the increases of dynamic loadings which could reduce system's life. The analysis on power losses and the increases of fatigue loadings in the wind farm is needed to prevent these unwanted consequences. Therefore, in this study velocity deficits have been predicted and aerodynamic analysis on turbines in the far wake is carried out from these velocity profiles. Ainslie's eddy viscosity wake model is adopted to determine a wake velocity and aerodynamic analysis on wind turbines is predicted by the numerical methods such as blade element momentum theory(BEMT) and vortex lattice method(VLM). The results show that velocity recovery is more rapid in the wake region with higher turbulence intensity. Since the velocity deficit is larger when the turbine has higher thrust coefficient, there is a huge aerodynamic power loss at the downstream turbine.

• KIEAE Journal
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• v.12 no.3
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• pp.27-32
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• 2012

In recent years, the quality of the outdoor thermal environment has come to be regarded as important as that of the indoor thermal environment. Since the outdoor thermal environment is composed of many elements and is affected by many factors, it is not easy to evaluate the impact of each factor separately. Hence, a comprehensive assessment method is required. In order to evaluate the pedestrian level comfort of an outdoor climate, it is necessary to investigate not only wind velocity but also various physical elements, such as temperature, moisture, radiation, etc. Prediction of wind and thermal environment for a large scale buildings is one of the most important targets for research. Wind and thermal change in a city area is a very complicated phenomenon affected by many physical processes. The purpose of this study is to develop a design plan for wind environment at a large Buildings. In this study, we analyze outdoor wind environment and thermal environment on buildings using the CFD (Computational Fluid Dynamics) method. The arrangement of building models is an apartment in Jeonju. These prediction of wind and thermal environment for a large scale buildings is necessary in a plan before a building is built.