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김현구(Kim Hyun-Goo),이영섭(Lee Yeong-Seup),장문석(Jang Mun-Seok),경남호(Kyong Nam-Ho) 한국태양에너지학회 2006 한국태양에너지학회 논문집 Vol.26 No.2
In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Gosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model, Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.
제주도 일단위 풍력발전예보 모형개발을 위한 군집분석 및 기상통계모형 실험
김현구(Kim Hyun-Goo),이영섭(Lee Yeong-Seup),장문석(Jang Moon-Seok) 한국태양에너지학회 2010 한국태양에너지학회 학술대회논문집 Vol.2010 No.11
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.