Due to the intermittency of wind power generation, it is very hard to manage its system operation and planning. In order to incorporate higher wind power penetrations into power systems that maintain secure and economic power system operation, an accurate and efficient estimation of wind power outputs is needed. In this paper, we propose the stochastic prediction of wind generating resources using an enhanced ensemble model for Jeju Island's wind farms in South Korea. When selecting the

more »

... l sites of wind farms, wind speed data at points of interest are not always available. We apply the Kriging method, which is one of spatial interpolation, to estimate wind speed at potential sites. We also consider a wind profile power law to correct wind speed along the turbine height and terrain characteristics. After that, we used estimated wind speed data to calculate wind power output and select the best wind farm sites using a Weibull distribution. Probability density function (PDF) or cumulative density function (CDF) is used to estimate the probability of wind speed. The wind speed data is classified along the manufacturer's power curve data. Therefore, the probability of wind speed is also given in accordance with classified values. The average wind power output is estimated in the form of a confidence interval. The empirical data of meteorological towers from Jeju Island in Korea is used to interpolate the wind speed data spatially at potential sites. Finally, we propose the best wind farm site among the four potential wind farm sites. Sustainability 2017, 9, 817 2 of 12 we propose a stochastic approach for wind power estimation using a spatial interpolation, terrain characteristic, and Weibull distribution. First, we introduce a method of spatial interpolation, called the Kriging method, and the wind profile power law to estimate the wind speed at a given point of interest. This spatial approach differs from various time series or neural network-based wind speed forecasting methods using historical data [11, 12] . The spatial approach can predict the wind speed at different points of interest spatially using only the current wind speed data. Second, we propose the method for wind power estimation using a Weibull distribution. Finally, we apply our method based on empirical data from the meteorological towers from Jeju Island in South Korea. The unknown wind speed data for potential points can be estimated using spatial interpolation and empirical meteorological data at various points in Jeju Island. Later, estimated wind speed data is used to model the Weibull distribution. Any wind turbine power curve data can be used to estimate the average turbine output. Based on the proposed method, we propose the best one among the four potential wind farm sites.