• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.283-284
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• 2008

• Journal of Environmental Science International
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• v.18 no.8
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• pp.885-897
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• 2009

In order to make sure the impact of spatial resolution of wind energy map on the estimation of wind power density in the Korean Peninsula, the comparison studies on the characteristics of wind energy map with three different spatial resolutions were carried out. Numerical model used in the establishment of wind map is MM5 (5th generation Mesoscale Model) with RBAPS (Regional Data Assimilation and Prediction System) as initial and boundary data. Analyzed Period are four months (March, August, October, and December), which are representative of four seasons. Since high spatial resolution of wind map make the undulation of topography be clear, wind pattern in high resolution wind map is correspond well with topography pattern and maximum value of wind speed is also increase. Indication of island and mountains in wind energy map depends on the its spatial resolution, so wind patterns in Heuksan island and Jiri mountains are clearly different in high and low resolutions. And area averaged power density can be changed by estimation method of wind speed for unit area in the numerical model and by treatment of air density. Therefore the studiable resolution for the topography should be evaluated and set before the estimation of wind resources in the Korean Peninsula.

• 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.

• New & Renewable Energy
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• v.7 no.1
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• pp.15-21
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• 2011

In recent years, many countries have been endeavoring to exploit the offshore wind energy in terms of overcoming the limitations of on-land wind energy. Considering that mountains cover 70 percent of the Korean Peninsula and arable plains for wind energy are negligibly small, Korean government aggressively drives the offshore wind development of the Korean Peninsula. As part of preliminary investigation of offshore wind resources, KEPCO-RI (Korea Electric Power Corporation-Research Institute) has been analyzing marine buoy datasets measured at 5 positions over the period of 12 years, including estimation of extreme wind speed. It can be observed that variation of yearly wind speed, monthly wind speed as well as frequency distribution of wind direction. Wind classes of buoy sites are estimated by extrapolated average wind speed using log law. In addition, wind turbine class based on IEC code is assessed for evaluation of suitable wind turbine.

• 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 Industrial Technology
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• v.22 no.A
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• pp.49-57
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• 2002

The accurate wind speed information at the hub height of a wind turbine is very essential to the exact estimation of the wind turbine power performance testing. Several methods on the site calibration, which is a technique to estimate the wind speed at the wind turbine's hub height based on the measured wind data using a reference meteorological mast, are introduced. A site calibration result and the wind resource assessment for the Taekwanryung test site are presented using a one-month wind data from a reference meteorological mast and a temporal mast installed at the site of wind turbine. From this analysis, it turns out that the current location of the reference meteorological mast is wrongly determined, and the self-developed codes for the site calibration are working properly. Besides, an analysis on the uncertainty allocation for the wind speed correction using site calibration is performed.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2250-2257
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• 2004

The accurate wind speed information at the hub height of a wind turbine is very essential to the exact estimation of the wind turbine power performance testing. Several methods on the site calibration, which is a technique to estimate the wind speed at the wind turbine's hub height based on the measured wind data using a reference meteorological mast, are introduced. A site calibration result and the wind resource assessment for the TaeKwanRyung test site are presented using three-month wind data from a reference meteorological mast and the other mast temporarily installed at the site of wind turbine. Besides, an analysis on the uncertainty allocation for the wind speed correction using site calibration is performed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.122-130
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• 2021

A wind-speed estimation at the arbitrary elevations is key component for the design of the offshore wind energy structures and the computation of the wind-wave generation. However, the wind-speed estimation of the target elevation has been carried out by using the typical functions and their typical parameters, e.g., power and logarithmic functions because the available wind speed data is limited to the specific elevation, such as 2~3m, 10 m, and so on. In this study, the parameters of the vertical profile functions are estimated with optimal and analyzed the parameter ranges using the HeMOSU-1 platform wind data monitored at the eight different locations. The results show that the mean value of the exponent of the power function is 0.1, which is significantly lower than the typically recommended value, 0.14. The values of the exponent, the friction velocity, and the roughness parameters are in the ranges 0.0~0.3, 0~10 (m/s), and 0.0~1.0 (m), respectively. The parameter ranges differ from the typical ranges because the atmospheric stability condition is assumed as the neutral condition. To improve the estimation accuracy, the atmospheric condition should be considered, and a more general (non-linear) vertical profile functions should be introduced to fit the diverse profile patterns and parameters.

• Wind and Structures
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• v.31 no.4
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• pp.299-309
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• 2020

In this study, wind data such as speeds, loads and potential of Muğla which is located in the southwest of Turkey were statistically analyzed. The wind data which consists of hourly wind speed between 2010 and 2013 years, was measured at the 10-meters height in four different ground stations (Datça, Fethiye, Marmaris, Köyceğiz). These stations are operated by The Turkish State Meteorological Service (T.S.M.S). Furthermore, wind data was analyzed by using Log-Normal and Gamma distributions, since these distributions fit better than Weibull, Normal, Exponential and Logistic distributions. Root Mean Squared Error (RMSE) and the coefficients of the goodness of fit (R²) were also determined by using statistical analysis. According to the results, extreme wind speed in the research area was 33 m/s at the Datça station. The effective wind load at this speed is 0.68 kN/㎡. The highest mean power densities for Datça, Fethiye, Marmaris and Köyceğiz were found to be 46.2, 1.6, 6.5 and 2.2 W/㎡, respectively. Also, although Log-normal distribution exhibited a good performance i.e., lower AD (Anderson - Darling statistic (AD) values) values, Gamma distribution was found more suitable in the estimation of wind speed and power of the region.

• Korean Management Science Review
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• v.30 no.2
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• pp.1-10
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• 2013

Case studies are made to investigate the relationship between the accuracy of energy production estimation and project feasibility indicators such as rate of return on equity (ROE) and debt service coverage ratio (DSCR) for three wind farm projects. It is found out that 1% improvement in the accuracy of energy production estimation may enhance the ROE by more than 0.5% in the case of P95, thanks to improved financing terms. AHP survey shows that MCP correlation of measured in situ wind data with long term wind speed distribution and hands-on experiences of flow analysis are more important than other factors for more precise annual energy production estimation.