| Title |
Evolutionary Nonlinear Regression Based Compensation Technique for Short-range Prediction of Wind Speed using Automatic Weather Station |
| Authors |
현병용(Hyeon, Byeongyong) ; 이용희(Lee, Yonghee) ; 서기성(Seo, Kisung) |
| DOI |
https://doi.org/10.5370/KIEE.2015.64.1.107 |
| 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. |