Impulse noise from hitting the piles and high cost due to construction time prevent offshore wind turbines from advancing over the world. Recently, a supporting structure with suction buckets, which is superior to piles and jackets in terms of low noise and cost, is being constructed and can then make the industries of offshore wind turbines to be more competitive. As for using the supporting structure with suction buckets, it is important to predict the natural frequencies of the whole structure. In this paper, the effective methods for modelling the various components of the whole structure are suggested in order to predict accurate natural frequencies. Spring elements are used for soil and connecting parts of suction buckets. Then, a blade is modelled by a 2-DOF equivalent model which has the same natural frequencies of the single blade. The proposed method is demonstrated by comparing predicted natural frequencies to experiment results.

The importance of wind energy has been increasing. Most of countries have studies for generating effective forecasting models about wind power prediction. This study follows up research about statistical wind forecasting models from 2000 to 2016 and compares each model in several aspects. Variety of estimators measure such as mean squared error and correlation coefficient that used to compare different models are defined. Most of methods use wind speed for dependent and independent variable. The extended application of artificial neural network and ARIMA model have mainly used to predict wind power in the past. The state-of the-art of focuses on nonlinear regression, feature selection and Ensemble methods which are different from classical ANN and ARIMA.

Wind resource potential is calculated by multiplying an area where a wind farm can be developed by wind turbine capacity density. Therefore, the wind turbine capacity density of wind farms in Korea must be calculated precisely first, in order to accurately calculate wind resource potential in Korea. In the present study, of the 67 total wind farms in Korea, 12 wind farms were selected, all of which had a total installed capacity of 10 MW or higher and had more than 10 wind turbines. Wind turbine coordinates and wind farm areas were determined using Daum Map Skyview, facilitating the calculation of wind turbine capacity density and wind turbine spacing. The mean wind turbine capacity density of wind farms in Korea was calculated at 6.9±2.1 MW/km², and the mean wind turbine spacing was calculated at 3.5D (D is a rotor diameter). In the future, when wind resource potential is calculated, the above values can be used to provide more realistic wind energy dissemination targets than it was previously possible.