風況区分予測モデルを用いた全国各地の時系列風速予測

概要

論文の詳細を見る
Most of the methods forecasting the wind speed proposed so far are not universal and available only for specific area. In the present study, forecasting method of a time-series wind speed 24-hours ahead at any location in Japan was proposed, which was based on classified forecast models by wind conditions (called FCCM; Forecasting Method using Classified forecast Model). The model was structured by the radial basis function network (RBFN) and the performance was compared to the conventional feed forward neural network (FFNN). The procedure of the FCCM was as follows; (1) All the points where wind speed had been measured by the Japan Meteorological Agency were divided into the groups depending on their wind conditions (mean wind speed and standard deviation of wind speed). (2) RBFN was learned and the network model was optimized to forecast wind speed in each group. (3) If the wind speed was not measured in the forecast point, it was estimated by using measured data around there. (4) Then, the time-series wind speed of the forecast point was obtained by the network model for the group. The wind speeds at 6 points in Japan where the wind condition were different were forecasted by FCCM. As a result, the mean absolute error was about from 1.0 to 1.5m/s. Although forecast error of RBFN and that of FFNN were similar, RBFN was able to learn fast than FFNN.
社団法人電気学会の論文
2009-05-01