Authors:
Kisung Seo
and
Byeongyong Hyeon
Affiliation:
Seokyeong University, Korea, Republic of
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 als
o 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.
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