• Wind and Structures
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• v.12 no.6
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• pp.553-574
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• 2009

This paper presents statistical analysis results of wind speed and atmospheric turbulence data measured from more than 30 anemometers installed at 15 different height levels on 325 m high Beijing Meteorological Tower and is primarily intended to provide useful information on boundary layer wind characteristics for wind-resistant design of tall buildings and high-rise structures. Profiles of mean wind speed are presented based on the field measurements and are compared with empirical models' predictions. Relevant parameters of atmospheric boundary layer at urban terrain are determined from the measured wind speed profiles. Furthermore, wind velocity data in longitudinal, lateral and vertical directions, which were recorded from an ultrasonic anemometer during windstorms, are analyzed and discussed. Atmospheric turbulence information such as turbulence intensity, gust factor, turbulence integral length scale and power spectral densities of the three-dimensional fluctuating wind velocity are presented and used to evaluate the adequacy of existing theoretical and empirical models. The objective of this study is to investigate the profiles of mean wind speed and atmospheric turbulence characteristics over a typical urban area.

• Wind and Structures
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• v.27 no.3
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• pp.187-197
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• 2018

In this study, aerodynamic characteristics of a horizontal axis wind turbine (HAWT) were evaluated and discussed in terms of measured data in existing onshore wind farm. Five wind turbines (T1, T2, T3, T4 and T5) were selected, and hub-height wind speed, $U_D$, wind turbine power output, P and turbine rotational speed, ${\Omega}$ data measured from these turbines were used for evaluation. In order to obtain characteristics of axial flow induction factor, a, power coefficient, $C_p$, thrust force coefficient, $C_T$, thrust force, T and tangential flow induction factor, a', Blade Element Momentum (BEM) theory was used. According to the results obtained, during a year, probability density of turbines at a rotational speed of 16.1 rpm was determined as approximately 45%. Optimum tip speed ratio was calculated to be 7.12 for most efficient wind turbine. Maximum $C_p$ was found to be 30% corresponding to this tip speed ratio.

• 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.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Atmosphere
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• v.25 no.3
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• pp.449-459
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• 2015

Effects of buildings and topography on observation environment of surface wind in central regions of urban areas are investigated using a computational fluid dynamics (CFD) model. In order to reflect the characteristics of buildings and topography in urban areas, geographic information system (GIS) data are used to construct surface boundary input data. For each observation station, 16 cases with different inflow directions are considered to evaluate effects of buildings and topography on wind speed and direction around the observation station. The results show that flow patterns are very complicated due to the buildings and topography. The simulated wind speed and direction at the location of each observation station are compared with those of inflow. As a whole, wind speed at observation stations decreases due to the drag effect of buildings. The decrease rate of wind speed is strongly related with total volume of buildings which are located in the upwind direction. It is concluded that the CFD model is a very useful tool to evaluate location of observation station suitability. And it is expected to help produce wind observation data that represent local scale excluding the effects of buildings and topography in urban areas.

• Journal of the Korean Solar Energy Society
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• v.30 no.2
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• pp.54-64
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• 2010

Interest in new and renewable energies like solar energy and wind energy is increasing throughout the world due to the rapidly expanding energy consumption and environmental reasons. An essential requirement for wind force power generation is estimating the size of wind energy accurately. Wind energy is estimated usually using meteorological data or field measurement. This study attempted to estimate wind energy density using meteorological data on daily mean wind speed and the Weibull parameters in Seoul, a representative inland city where over 60% of 15 story or higher apartments in Korea are situated, and Busan, Incheon, Ulsan and Jeju that are major coastal cities in Korea. According to the results of analysis, the monthly mean probability density distribution based on the daily mean wind speed agreed well with the monthly mean probability density distribution based on the Weibull parameters. This finding suggests that the Weibull parameters, which is highly applicable and convenient, can be utilized to estimate the wind energy density distribution of each area. Another finding was that wind energy density was higher in coastal cities Busan and Incheon than in inland city Seoul.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.34-34
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• 2023

The Chah-Nimeh reservoirs, which are a sort of natural lakes located in the border of Iran and Afghanistan, are the main drinking and agricultural water resources of Sistan arid region. Considering the occurrence of intense seasonal wind, locally known as levar wind, this study aims to explore the possibility to provide a TSM (Total Suspended Matter) monitoring model of Chah-Nimeh reservoirs using multi-temporal satellite images and in-situ wind speed data. The results show that a strong correlation between TSM concentration and wind speed are present. The developed empirical model indicated high performance in retrieving spatiotemporal distribution of the TSM concentration with R²=0.98 and RMSE=0.92g/m³. Following this observation, we also consider a machine learning-based model to predicts the average TSM using only wind speed. We connect our in-situ wind speed data to the TSM data generated from the inversion of multi-temporal satellite imagery to train a neural network based mode l(Wind2TSM-Net). Examining Wind2TSM-Net model indicates this model can retrieve the TSM accurately utilizing only wind speed (R²=0.88 and RMSE=1.97g/m³). Moreover, this results of this study show tha the TSM concentration can be estimated using only in situ wind speed data independent of the satellite images. Specifically, such model can supply a temporally persistent means of monitoring TSM that is not limited by the temporal resolution of imagery or the cloud cover problem in the optical remote sensing.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1173-1178
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• 2011

The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.158-163
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• 2012

This study is concerned with the estimation of wind speeds for return period in cellularized district of Busan by the recent meteorological data. Recently standard of the wind load in Busan area is determined by using meteorological wind speed data which is observed on Automated Synoptic Observing System(ASOS) only. Applying the existing basic wind speed that is 40m/s to the construction design of Busan area is inefficient. Because the wind speeds of Busan area show different amounts depend on the location of cellularized district. This research analyze the observed data of wind speeds of cellularized district in Busan based on Automate Weather System(AWA). In addition that we compute regional wind speeds for return period by using Gumbel distribution and study and compare with the existing basic wind speeds after evaluating appropriateness by Hazen's plot method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.326-332
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• 2012

In this paper, error analyses on the calculation of offshore wind speed have been conducted using HeMOSU-1 data to develop offshore wind energy in Yeonggwang sea of Korea and onshore observed wind data in Buan, Gochang and Yeonggwang for 2011. Offshore wind speed data at 98.69 m height above M.S.L is estimated using relational expression induced by linear regression analysis between onshore and offshore wind data. In addition, estimated offshore wind speed data is set at 87.65 m above M.S.L using power law wind profile model with power law exponent(0.115) and its results are compared with the observed data. As a result, the spatial adjustment error are 1.6~2.2 m/s and the altitude adjustment error is approximately 0.1 m/s. This study shows that the altitude adjustment error is about 5% of the spatial adjustment error. Thus, long term observed data are needed when offshore wind speed was estimated by onshore wind speed data. because the conversion of onshore wind data lead to large error.