• 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.

• 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.

• New & Renewable Energy
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• v.10 no.2
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• pp.19-28
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• 2014

Wind power generation of 5 MW wind turbine was predicted by using wind measurement data from HeMOSU-1 which is at south west coast of Korea. Time histories of turbulent wind was generated from 10-min mean wind speed and then they were used as input to Bladed to estimated electric power. Those estimated powers are used in both polynominal regression and neural network training. They were compared with each other for daily production and yearly production. Effect of mean wind speed and turbulence intensity were quantitatively analyzed and discussed. This technique further can be used to assess lifetime power of wind turbine.

• Wind and Structures
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• v.31 no.5
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• pp.419-430
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• 2020

Weibull distribution is a conspicuous distribution known for its accuracy and its usage for wind energy analysis. The two and three parameter Weibull distributions are adopted in this study to fit wind speed data. The daily mean wind speed data of Ennore, Tamil Nadu, India has been used to validate the procedure. The parameters are estimated using maximum likelihood method, least square method and moment method. Four statistical tests namely Root mean square error, R2 test, Kolmogorov-Smirnov test and Anderson-Darling test are employed to inspect the fitness of Weibull probability density functions. The value of shape factor, scale factor, wind speed and wind power are determined at a height of 100m using extrapolation of numerical equations. Also, the value of capacity factor is calculated mathematically. This study provides a way to evaluate feasible locations for wind energy assessment, which can be used at any windy site throughout the world.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2A
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• pp.197-205
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• 2008

In order to estimate mean wind speed in the Gwangyang-Myodo Region, the probability distribution model of extreme values has been used in the statistical analysis of joint distribution probability of daily maximum wind speed and corresponding direction in this paper. For this purpose frequency of daily maximum records at respective stations is inquired into and sample of largest yearly wind speed of sixteen compass direction and non-direction is extracted from daily data of maximum wind speed and appropriate direction of the meteorological observing stations nearby the bridge construction site. These extreme speed records are applied to Gumbel and Weibull distribution model and parameters are estimated through method of moment and method of least squares etc. And also, distribution and parameters are inquired into whether it is fitted through the probability plot correlation coefficient examination. From fitted parameters the largest yearly wind speed of sixteen compass direction and non-direction is extrapolated taking into account factors regarding sample size of data and distance from the bridge construction site according to the appropriate stations.

• Atmosphere
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• v.26 no.4
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• pp.617-633
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• 2016

Jangbogo station is located in Terra Nova Bay over the East Antarctica, which is often affected by individual storms moving along nearby storm tracks and a katabatic flow from the continental interior towards the coast. A numerical simulation for two strong wind events of maximum instantaneous wind speed ($41.17m\;s^{-1}$) and daily mean wind speed ($23.92m\;s^{-1}$) at Jangbogo station are conducted using the polar-optimized version of Weather Research and Forecasting model (Polar WRF). Verifying model results from 3 km grid resolution simulation against AWS observation at Jangbogo station, the case of maximum instantaneous wind speed is relatively simulated well with high skill in wind with a bias of $-3.3m\;s^{-1}$ and standard deviation of $5.4m\;s^{-1}$. The case of maximum daily mean wind speed showed comparatively lower accuracy for the simulation of wind speed with a bias of -7.0 m/s and standard deviation of $8.6m\;s^{-1}$. From the analysis, it is revealed that the each case has different origins for strong wind. The highest maximum instantaneous wind case is caused by the approach of the strong synoptic low pressure system moving toward Terra Nova Bay from North and the other daily wind maximum speed case is mainly caused by the katabatic flow from the interiors of Terra Nova Bay towards the coast. Our evaluation suggests that the Polar WRF can be used as a useful dynamic downscaling tool for the simulation and investigation of high wind events at Jangbogo station. However, additional efforts in utilizing the high resolution terrain is required to reduce the simulation error of high wind mainly caused by katabatic flow, which is received a lot of influence of the surrounding terrain.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• Atmosphere
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• v.33 no.5
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• pp.477-492
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• 2023

Based on the new climate normals (1991~2020), annual mean, maximum and minimum temperature is 12.5℃, 18.2℃, and 7.7℃, respectively while annual precipitation is 1,331.7 mm, the annual mean wind speed is 2.0 m s^-1, and the relative humidity is 67.8% in the Republic of Korea. Compared to 1981~2010 normal, annual mean temperature increased by 0.2℃, maximum and minimum temperatures increased by 0.3℃, while the amount of precipitation (0.7%) and relative humidity (1.1%) decreased. There was no distinct change in annual mean wind speed. The spatial range of the annual mean temperature in the new normals is large from 7.1 to 16.9℃. Annual precipitation showed a high regional variability, ranging from 787.3 to 2,030.0 mm. The annual mean relative humidity decreased at most weather stations due to the rise in temperature, and the annual mean wind speed did not show any distinct difference between the new and old normals. With the addition of a warmer decade (2011~2020), temperatures all increased consistently and in particular, the increase in the maximum temperature, which had not significantly changed in previous decades, was evident. The increasing trend of annual and summer precipitation by the 2010s has disappeared in the new normals. Among extreme climate indices, MxT30 (Daily maximum temperature ≥ 33℃ days), MnT25 (Daily minimum temperature ≥ 25℃ days), and PH30 (1 hour maximum precipitation ≥ 30 mm days) increased while MnT-10 (Daily minimum temperature < -10℃ days) and W13.9 (Daily maximum wind speed ≥ 13.9 m/s days) decreased at a statistically significant level. It is thought that a detailed study on the different trends of climate elements and extreme climate indices by region should be conducted in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.277-282
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• 2012

When wind speed exceeds a certain threshold, daily minimum temperature does not drop as predicted by the geospatial model in a cold pooling catchment. A linear regression equation was derived to explain the warming effect of wind speed on daily minimum temperature by analyzing observations at a low lying location within an enclosed catchment. The equation, Y=2X+0.4 ($R^2$=0.76) where Y stands for the warming ($^{\circ}C$) and X for the mean horizontal wind speed (m/s) at 2m height, was combined to an existing model to predict daily minimum temperature across an enclosed catchment on cold pooling days. The adjusted model was applied to 3 locations submerged in a cold air pool to predict daily minimum temperature on 25 cold pooling days with the input of simulated wind speed at each location. Results showed that bias (mean error) was reduced from -1.33 to -0.37 and estimation error (RMSE) from 1.72 to 1.20, respectively, in comparison with those from the unadjusted model.

• Journal of Korean Society for Atmospheric Environment
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• v.7 no.1
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• pp.49-53
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• 1991

Relations of urban heat island and air pollution are analyzed by using $SO_2$ concentration data (winter season in 1985) from 10 sites of Seoul area and differences of wind speed and air temperature in urban and rural area. Urban heat island is developed when daily mean wind speed at urban site is lower than 1.5m/sec or in the interval of 3.0 $\sim$ 3.5m/sec. When differences between urban and rural air temperature is greater than the overall average of those differences, $SO_2$ concentrations of those above-average differences are 1.3 $\sim$ 1.8 times higher than those of below-average differences. The trends are shown obviously at north-eastern area of Seoul (Gilum Dong, Ssangmun Dong, Myeonmog Dong). When intensity of Urban Heat Island is weak, $SO_2$ concentration was reduced in propotion to a rise of wind speed. But $SO_2$ concentration is on the partial increase in spite of a rise of wind speed when intensity of urban heat island is strong.