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Cluster Analysis and Meteor-Statistical Model Test to Develop a Daily Forecasting Model for Jejudo Wind Power Generation

제주도 일단위 풍력발전예보 모형개발을 위한 군집분석 및 기상통계모형 실험

  • Kim, Hyun-Goo (Wind Energy Research Center, Korea Institute of Energy Research) ;
  • Lee, Yung-Seop (Department of Statistics, Dongguk University) ;
  • Jang, Moon-Seok (Wind Energy Research Center, Korea Institute of Energy Research)
  • 김현구 (한국에너지기술연구원 풍력발전연구센터) ;
  • 이영섭 (동국대학교 통계학과) ;
  • 장문석 (한국에너지기술연구원 풍력발전연구센터)
  • Received : 2010.07.31
  • Accepted : 2010.09.10
  • Published : 2010.10.31

Abstract

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.

Keywords

Wind power forecasting;Cluster analysis;Meteor-statistical forecasting model;Jejudo

Acknowledgement

Supported by : 한국에너지기술연구원

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