Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

Although renewable power is regarded a way to active response to climate change, the stability of whole power system could be a serious problem in the future due to its uncertainties such as indispatchableness and intermittency. From this perspective, the peak time impact of stochastic wind power generation is estimated using simulation method up to year 2030 based on the 3rd master plan for the promotion of new and renewable energy on peak time. Result shows that the highest probability of wind power impact on peak time power supply could be up to 4.41% in 2030. The impact of wind power generation on overall power mix is also analyzed up to 2030 using SCM model. The impact seems smaller than expectation, however, the estimated investment cost to make up such lack of power generation in terms of LNG power generation facilities is shown to be a significant burden to existing power companies.

Wind energy, which is one of renewable energy, would be useful resources that can be applied to making energy recycling villages without using fossil fuels. This study analyzed energy potential on wind power considering weather condition in three rural villages and compared with energy consumption surveyed. A wind turbine system in the 5kW class can generate 26.1%, 73.9% and 39.5% of the yearly mean consumption of electric power per house in Makhyun, Boojang and Soso respectively. A 750kW wind turbine system can generate 1.7%, 30.3% and 22.1% of the total amount of electric power consumption in three study villages respectively. Wind power energy density was too low in Makhyun and Soso, so it is determined that the application of wind turbine system is almost impossible. Wind energy potential was generally low in Boojang either, but it is evaluated that there is a little possibility of wind power generation relatively. For practical application of renewable energy to rural green-village planning, assessment of energy potential for the local area should be preceded.