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Authors: Kisung Seo and Byeongyong Hyeon

Affiliation: Seokyeong University, Korea, Republic of

ISBN: 978-989-758-157-1

Keyword(s): Wind Speed Prediction, Nonlinear MOS, Genetic Programming.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: Wind speed fluctuates heavily and affects a smaller locality than other weather elements. 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. Most previous MOS (Model Output Statistics) used a linear regression model, but they are hard to solve nonlinear natures of the weather prediction. In order to solve the problem of a linear MOS, a nonlinear compensation technique based on evolutionary computation is introduced as a new attempt. We suggest a nonlinear regression method using GP (Genetic Programming) based symbolic regression to generate an open-ended nonlinear MOS. The new nonlinear MOS can express not only nonlinearity much more extensively by involving all mathematical functions, including transcendental functions, but also unlimited orders with a dynamic selection of predictors due to the flexible tree structure of GP. We evaluate the accuracy of the estimation by GP based nonlinear MOS for the three days wind speed prediction for Korean regions. The training period of 2007- 2009, 2011 year is used, the data of 2012 year is for verification, and 2013 year is adopted for test. This method is then compared to the linear MOS and shows superior results. (More)

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Paper citation in several formats:
Seo, K. and Hyeon, B. (2015). Evolutionary Nonlinear Model Output Statistics for Wind Speed Prediction using Genetic Programming.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 287-292. DOI: 10.5220/0005611602870292

@conference{ecta15,
author={Kisung Seo. and Byeongyong Hyeon.},
title={Evolutionary Nonlinear Model Output Statistics for Wind Speed Prediction using Genetic Programming},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={287-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005611602870292},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Evolutionary Nonlinear Model Output Statistics for Wind Speed Prediction using Genetic Programming
SN - 978-989-758-157-1
AU - Seo, K.
AU - Hyeon, B.
PY - 2015
SP - 287
EP - 292
DO - 10.5220/0005611602870292

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