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Soft Computing

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11-12-2019 | Methodologies and Application

New SVM kernel soft computing models for wind speed prediction in renewable energy applications

Authors: Yogambal Jayalakshmi Natarajan, Deepa Subramaniam Nachimuthu

Published in: Soft Computing | Issue 15/2020

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Abstract

This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA was applied to eliminate the noise and to approximate the signal with trend information; VMD was applied to decompose and to extract the features of input time series wind speed data into a number of sub-layers; and the SVM model with various kernel functions was adopted to predict the wind speed from each of the sub-layers, and the parameters of SVM were fine-tuned by differential evolutionary algorithm. To investigate the effectiveness of the proposed model, various prediction models are considered for comparative study, and it is demonstrated that the proposed model outperforms with better prediction accuracy.

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Title: New SVM kernel soft computing models for wind speed prediction in renewable energy applications
Authors: Yogambal Jayalakshmi Natarajan
Deepa Subramaniam Nachimuthu
Publication date: 11-12-2019
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 15/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04608-w

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