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Modeling of Wind Speed Profile using Soft Computing Techniques

Modeling of Wind Speed Profile using Soft Computing Techniques

Pijush Samui, Yıldırım Dalkiliç
Copyright: © 2015 |Pages: 22
ISBN13: 9781466666313|ISBN10: 1466666315|EISBN13: 9781466666320
DOI: 10.4018/978-1-4666-6631-3.ch010
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MLA

Samui, Pijush, and Yıldırım Dalkiliç. "Modeling of Wind Speed Profile using Soft Computing Techniques." Soft Computing Applications for Renewable Energy and Energy Efficiency, edited by Maria del Socorro García Cascales, et al., IGI Global, 2015, pp. 252-273. https://doi.org/10.4018/978-1-4666-6631-3.ch010

APA

Samui, P. & Dalkiliç, Y. (2015). Modeling of Wind Speed Profile using Soft Computing Techniques. In M. Cascales, J. Lozano, A. Arredondo, & C. Corona (Eds.), Soft Computing Applications for Renewable Energy and Energy Efficiency (pp. 252-273). IGI Global. https://doi.org/10.4018/978-1-4666-6631-3.ch010

Chicago

Samui, Pijush, and Yıldırım Dalkiliç. "Modeling of Wind Speed Profile using Soft Computing Techniques." In Soft Computing Applications for Renewable Energy and Energy Efficiency, edited by Maria del Socorro García Cascales, et al., 252-273. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6631-3.ch010

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Abstract

This chapter examines the capability of three soft computing techniques (Genetic Programming [GP], Support Vector Machine [SVM], and Multivariate Adaptive Regression Spline [MARS]) for prediction of wind speed in Nigeria. Latitude, longitude, altitude, and the month of the year have been used as inputs of GP, RVM, and MARS models. The output of GP, SVM, and MARS is wind speed. GP, SVM, and MARS have been used as regression techniques. To develop GP, MARS, and SVM, the datasets have been divided into the following two groups: 1) Training Dataset – this is required to develop GP, MPMR, and RVM models. This study uses 18 stations' data as a training dataset. 2) Testing Dataset – this is required to verify the developed GP, MPMR, and RVM models. The remaining 10 stations data have been used as testing dataset. Radial basis function has been used as kernel functions for SVM. A detailed comparative study between the developed GP, SVM, and MARS models is performed in this chapter.

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