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http://dx.doi.org/10.5370/KIEE.2016.65.5.851

Comparison of MLR and SVR Based Linear and Nonlinear Regressions - Compensation for Wind Speed Prediction  

Kim, Junbong (Electronics Engineering, Seokyeong University)
Oh, Seungchul (Omron Korea)
Seo, Kisung (Dept. of Electronics Engineering, Seokyeong University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.65, no.5, 2016 , pp. 851-856 More about this Journal
Abstract
Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.
Keywords
Wind Speed Prediction; Unified Model; Model Output Statistics; Linear Regression; Non-Linear Regression; Support Vector Regression; UM Elements;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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