Constrained Support Vector Machines for photovoltaic in-feed prediction

In this paper, we introduce a constrained Support Vector Machine (SVM) to predict photovoltaic (PV) in-feed. We derive the SVM algorithm with linear constraints and test the method on German PV in-feed with constraints reflecting physical boundaries. We show that the new algorithm shows a significan...

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Bibliographic Details
Published in2013 1st IEEE Conference on Technologies for Sustainability (SusTech) pp. 23 - 28
Main Authors Hildmann, Marcus, Rohatgi, Abhishek, Andersson, Goran
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2013
Subjects
Correlation
Estimation
Kernel
Optimization
Predictive models
Support vector machines
Temperature
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Summary:In this paper, we introduce a constrained Support Vector Machine (SVM) to predict photovoltaic (PV) in-feed. We derive the SVM algorithm with linear constraints and test the method on German PV in-feed with constraints reflecting physical boundaries. We show that the new algorithm shows a significant better performance than a constrained ordinary least squares (OLS) estimator.
DOI:10.1109/SusTech.2013.6617293