• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1089-1098
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• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.

• Wind and Structures
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• v.33 no.6
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• Journal of Wind Energy
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• v.15 no.1
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• pp.11-20
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• 2024

As the generation capacity of floating offshore wind turbines increases, the wind load applied to each turbine increases. Due to such a high wind load, the capacity of transport equipment (such as tugboats or cranes) required in the transportation and installation phases must be much larger than that of previous small-capacity wind power generation systems. However, for such an important wind load prediction method, the simple formula proposed by the classification society is generally used, and prediction through wind tunnel tests or Computational Fluid Dynamics (CFD) is rarely used, especially for a concept or initial design stages. In this study, the wind load of a 10 MW class floating offshore wind turbine was predicted by a simplified formula and compared with results of wind tunnel tests. In addition, the wind load coefficients at each stage of fabrication, transportation, and installation are presented so that it can be used during a concept or initial design stages for similar floating offshore wind turbines.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.318-320
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• 2008

Offshore remote-sensing wind extraction using SAR satellite image is an emerging and promising technology for offshore wind resource assessment. We compared our numerical weather prediction and offshore wind extraction from ENVISAT images around Korea offshore areas. A few comparison sets showed good agreement but more comparisons are required to draw application guideline on a statistical basis.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.181-186
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• 2009

The wind data measured from automated weather stations (AWS) at complex terrains in Korea was used to predict the wind velocity at nearby sites that are several kilometers away. The ten-minute averaged wind data was measured at a height of 10 meters. A commercial CFD code, WindSIM, based on the weighted averaged Navier-Stokes equation was employed. The results were compared with the data measured using meteorological masts (MM) at a height of 40 meters. The predictions using the AWS data and WindSIM showed good agreements with the measured data.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.241-253
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• 2015

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

• Journal of the Korean earth science society
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• v.38 no.1
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• pp.11-23
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• 2017

The short term predictability of wind resources is an important factor in evaluating the economic feasibility of a wind power plant. As a method of improving the predictability, a Bayesian Kalman filter is applied as the model data postprocessing. At this time, a statistical training period is needed to evaluate the correlation between estimated model and observation data for several Kalman training periods. This study was quantitatively analyzes for the prediction characteristics according to different training periods. The prediction of the temperature and wind speed with 3-day short term Bayesian Kalman training at Taebaek area is more reasonable than that in applying the other training periods. In contrast, it may produce a good prediction result in Ieodo when applying the training period for more than six days. The prediction performance of a Bayesian Kalman filter is clearly improved in the case in which the Weather Research Forecast (WRF) model prediction performance is poor. On the other hand, the performance improvement of the WRF prediction is weak at the accurate point.

• Journal of the Korean Solar Energy Society
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• v.35 no.3
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• pp.1-8
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• 2015

The MERRA reanalysis data provided online by NASA was applied to predict the monthly energy productions of Donghai Bridge Offshore wind farms in China. WindPRO and WindSim that are commercial software for wind farm design and energy prediction were used. For topography and roughness map, the contour line data from SRTM combined with roughness information were made and used. Predictions were made for 11 months from July, 2010 to May, 2011, and the results were compared with the actual electricity energy production presented in the CDM(Clean Development Mechanism)monitoring report of the wind farm. The results from the prediction programs were close to the actual electricity energy productions and the errors were within 4%.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.180.1-180.1
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• 2010

Wind turbine noise is generally lower than that from other environmental noise sources such as road and railway noise. Nevertheless, some residents living more than 1km away from wind turbines have claimed that they suffer sleep disturbance due to wind turbine noise. Several researchers have maintained that residents near a wind farm may perceive large amplitude modulation of wind turbine noise at night, and this amplitude modulation is the main cause of the noise annoyance. However, to date only few studies exist on the prediction of the amplitude modulation of wind turbine noise. Thus, this study predicts amplitude modulated noise generated from a generic 2.5MW wind turbine. Semi-empirical noise models are employed to predict the modulation depth and the overall sound pressure level of the wind turbine noise. The result shows that the amplitude modulation is observed regardless of atmospheric stability, but the modulation depth in a stable atmosphere is 1~3dB higher than that in an unstable atmosphere near the plane of rotation where the blades move downward. Moreover, using the result of the noise prediction, this study estimates the maximum perceptible distance of the wind turbine noise cause by amplitude modulation. The result indicates that the wind turbine noise can be perceived at a distance of up to 1600m in the range of about 30~60 degree from the on axis in a extremely low background noise environment.