Linear and non-linear proximal support vector machine classifiers for wind speed prediction

The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vecto...

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Bibliographic Details
Published in:Cluster computing 2019-01, Vol.22 (Suppl 1), p.379-390
Main Authors: Ranganayaki, V., Deepa, S. N.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alternative energy sources
Business metrics
Classifiers
Computer Communication Networks
Computer Science
Effectiveness
Humidity
Kernel functions
Machine learning
Mean square errors
Neural networks
Operating Systems
Processor Architectures
Real time
Renewable resources
Support vector machines
Wind farms
Wind power
Wind speed
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Summary:The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vector machine (LSVM) and proximal support vector machine (PSVM) is proposed to carry out the wind speed prediction using the available real time wind farm data. In developed PSVM predictor, it is modeled for both linear PSVM predictor and non-linear PSVM predictor. The difference between the developed linear and non-linear PSVM predictor models lies in their applicability of kernel functions to perform effective wind speed prediction. The prediction application is implemented for the set of wind farm data with a wind mill height of 50 m in a manner to minimize the mean square error. The training process of the neural network algorithmic flow is done with the developed LSVM, L-PSVM (Linear PSVM) and N-PSVM (nonlinear PSVM) for predicting the wind speed in renewable energy systems. Results computed are compared with the other types of predictors to prove the effectiveness of the proposed variants of SVM predictors. The simulated results presents the effectiveness of the proposed predictors for the real time wind farm data and the applicability of the predictors for the considered datasets.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-018-2005-6