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.

Abstract
 1. 서 론
 2. 자료 및 방법
  2.1. 기상관측자료
  2.2. 군집분석
  2.3. 기상통계모형
 3. 결과 및 고찰
  3.1. 기상관측자료 군집분석
  3.2. 기상통계모형 실험
  3.3. 기상통계모형 비교평가
 4. 결 론
 참 고 문 헌

• 김현구(한국에너지기술연구원 풍력발전연구센터) | Hyun-Goo Kim (Wind Energy Research Center, Korea Institute of Energy Research) Corresponding Author
• 장문석(한국에너지기술연구원 풍력발전연구센터) | Moon-Seok Jang (Wind Energy Research Center, Korea Institute of Energy Research)
• 이영섭(동국대학교 통계학과) | Yung-Seop Lee (Department of Statistics, Dongguk University)