• Journal of the Korean Geographical Society
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• v.47 no.3
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• pp.347-358
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• 2012

This study aimed to investigate the change of wind speed during winter and summer seasons for 50 years(1961-2010). It were analyzed the mean wind speed, maximum wind speed, windy days and storm days on January and August of 13 weather stations in South Korea. The mean wind speed was decreased in the coastal region(Busan, Jeju, Ulsan, Pohang) in winter and summer seasons. Also it was similar to windy day. The relationship between wind speed and mean temperature has negative correlation in winter season. The relationship is low in summer season. The number of windy day and storm day has negative relation with monthly mean temperature.

• Proceedings of KOSOMES biannual meeting
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• 2003.11a
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• pp.163-166
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• 2003

The seasonal mean wind direction and wind speed in a greater coasting area are investigated using the ECMWF(European Centre for Medium-Range Weather Forecasts) data for 11 years from 1985 to 1995. In winter, the main wind direction in Korea and vicinity, Taiwan and vicinity, and the North Pacific Ocean of middle latitudes is a northwesterly wind, northeasterly wind, and westerly wind respectively. The wind speed is strongest in the East China Sea, the South China Sea, and the North Pacific Ocean of low latitudes(Beaufort wind scale 5-6). A distribution pattern of wind direction in spring and fall is similar to that in winter. Seasonal mean wind speed is strongest in winter and the next is fall. The wind speed in summer is generally weak. However, that in the Indochina and vicinity is strong by the influence of Asian monsoon.

• Atmosphere
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• v.18 no.3
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• pp.171-182
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• 2008

In this study, we analyzed the spatial and temporal distribution of wind resources over Korea based on hourly observational data recorded over a period of 5 years from 457 stations belonging to Korea Meteorological Administration (KMA). The surface and 850 hPa wind data obtained from the Korea Local Analysis and Prediction System (KLAPS) and the Regional Data Assimilation and Prediction System (RDAPS) over a period of 1 year are used as supplementary data sources. Wind speed is generally high over seashores, mountains, and islands. In 62 (13.5%) stations, mean wind speeds for 5 years are greater than $3ms^{-1}$. The effects of seasonal wind, land-sea breeze, and mountain-valley winds on wind resources over Korea are evaluated as follows: First, wind is weak during summer, particularly over the Sobaek Mountains. However, over the coastal region of the Gyeongnam-province, strong southwesterly winds are observed during summer owing to monsoon currents. Second, the wind speed decreases during night-time, particularly over the west coast, where the direction of the land breeze is opposite to that of the large-scale westerlies. Third, winds are not always strong over seashores and highly elevated areas. The wind speed is weaker over the seashore of the Gyeonggi-province than over the other seashores. High wind speed has been observed only at 5 stations out of the 22 high-altitude stations. Detailed information on the wind resources conditions at the 21 stations (15 inland stations and 6 island stations) with high wind speed in Korea, such as the mean wind speed, frequency of wind speed available (WSA) for electricity generation, shape and scale parameters of Weibull distribution, constancy of wind direction, and wind power density (WPD), have also been provided. Among total stations in Korea, the best possible wind resources for electricity generation are available at Gosan in Jeju Island (mean wind speed: $7.77ms^{-1}$, WSA: 92.6%, WPD: $683.9Wm^{-2}$) and at Mt. Gudeok in Busan (mean wind speed: $5.66ms^{-1}$, WSA: 91.0%, WPD: $215.7Wm^{-2}$).

• Wind and Structures
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• v.32 no.2
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• pp.161-167
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• 2021

In this study, the artificial neural network (ANN) method was used for estimating the monthly mean wind speed of Sivas, in the central part of Turkey. Eighteen years of wind speed data obtained from nine measurement stations during the period of 2000-2017 at 10 m height was used for ANN analysis. It was found that mean absolute percentage error (MAPE) ranged from 3.928 to 6.662, mean bias error (MBE) ranged from -0.089 to -0.003, while root mean square error (RMSE) ranged from 0.050 to 0.157 and R2 ranged from 0.86 to 0.966. ANN models provide a good approximation of the wind speed for all measurement stations, however, a tendency to underestimate is also obvious.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.820-826
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• 2014

It is well known that energy generated by a wind generator (WG) depends on the wind resources at the installation site. In other words, a WG installed in a high wind speed area can produce more energy than that in a low wind speed area. However, a WG installed at a low wind site can produce a similar amount of energy to that produced by a WG installed at a high wind site if the WG is designed with a rated wind speed corresponding to the mean wind speed of the site. In this paper, we investigated the power curve of a WG suitable for Korea's southwestern coast with a low mean wind speed to achieve a high capacity factor (CF). We collected the power curves of the 11 WGs of the 6 WG manufacturers. The probability density function of the wind speed on Korea's southwestern coast was modeled using the Weibull distribution. The annual energy production by the WG was calculated and then the CFs of all of the WGs were estimated and compared. The results indicated that the WG installed on the Korea's southwestern coast could obtain a CF higher than 40 % if it was designed with the lower rated speed corresponding to the mean wind speed at the installation site.

• Wind and Structures
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• v.26 no.6
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• pp.383-395
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• 2018

An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

• Journal of Korean Association for Spatial Structures
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• v.16 no.1
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• pp.65-72
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• 2016

Aerodynamic performance of solar wing system has been evaluated through wind tunnel test. The test model has 12 panels, each supported by 2 cables. The panels were installed horizontally flat, and gaps between panels were set constant. Sag ratios of 2% and 5%, and wind directions between $0^{\circ}$ and $90^{\circ}$ were considered. Mass of test model was determined considering the mass of full scale model, and Froude number and Elastic parameter were satisfied by adjusting the mean wind speed. From the wind tunnel test, it was found that the aerodynamic performance of the solar wing system is very dependent on the wind directions and sag ratios. When the sag was 2%, the fluctuating displacements between the wind directions of $0^{\circ}$ and $30^{\circ}$ increase proportionally to the square of the mean wind speed, implying buffeting-like vibration and a sudden increase in fluctuating displacement was found at large mean wind speed for the wind directions larger than $40^{\circ}$. When the wind direction was larger than $60^{\circ}$, a sudden increase was found both at low and large mean wind speed. When the sag ratio is 5%, distribution of mean displacements is different from that of sag ratio of 2%, and the fluctuating displacements show very different trend from that of sag ratio of 2%.

• Wind and Structures
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• v.18 no.6
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• pp.619-631
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• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1338-1341
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• 2002

The Weibull probability density function and the Rayleigh function are compared by analyzing the relations of the capacity factors which are compared the actual wind speed frequency curve with which are modelled using the probability density functions with different mean wind speeds. For this analysis, the wind speed means of arithmetic, root mean square, cubic mean cuberoot, and standard deviations are computed from the measured wind speed data of a specific site and the coefficients of probability density functions are calculated. The capacity factors for Vestas 850[kW] wind turbine are calculated and analyzed. The results shows that the wind speed frequency curve by Rayleigh function is more close to the actual curve than by Weibull function. The more the wind speed frequency curve is close to the actual one, the more the capacity factors become large values.

• Wind and Structures
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• v.13 no.3
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• pp.275-292
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• 2010

Wind tunnel experiments were conducted to investigate the wind characteristics in the mountainous valley terrain with 4 simplified valley models and a 1:500 scale model of an existing valley terrain in the simulated atmospheric neutral boundary layer model. Measurements were focused on the mean wind flow and longitudinal turbulence intensity. The relationship between hillside slopes and the velocity speed-up effect were studied. By comparing the preliminary results obtained from the simplified valley model tests and the existing terrain model test, some fundamental information was obtained. The measured results indicate that it is inappropriate to describe the mean wind velocity profiles by a power law using the same roughness exponent along the span wise direction in the mountainous valley terrain. The speed-up effect and the significant change in wind direction of the mean flow were observed, which provide the information necessary for determining the design wind speed such as for a long-span bridge across the valley. The longitudinal turbulence intensity near the ground level is reduced due to the speed-up effect of the valley terrain. However, the local topographic features of a more complicated valley terrain may cause significant perturbation to the general wind field characteristics in the valley.