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Journal Paper

Journal Paper

Journal Paper

Journal Paper
Year of Publication 2015 Division ġڷ()
Title AWS ͸ ̿ ȭ ȸͺм ܱ dz
Author
Coauthor ⼺, ̿
ISBN(ISSN) 2287-4364 Name of Journal ȸ
Category (International/Domestic) Vol. No. 64
Research Project Title Ȱ뿬 (2015) Publication Date 2015-01-02
Keywords Wind speed prediction, MOS(Model Output Statistics), Genetic programming, AWS(Automatic Weather Station)

Abstract

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing

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