Please use this identifier to cite or link to this item: https://doi.org/10.1109/SusTech.2013.6617293
Title: Constrained support vector machines for photovoltaic in-feed prediction
Authors: Hildmann, M.
Rohatgi, A. 
Andersson, G.
Issue Date: 2013
Source: Hildmann, M.,Rohatgi, A.,Andersson, G. (2013). Constrained support vector machines for photovoltaic in-feed prediction. 2013 1st IEEE Conference on Technologies for Sustainability, SusTech 2013 : 23-28. ScholarBank@NUS Repository. https://doi.org/10.1109/SusTech.2013.6617293
Abstract: 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. © 2013 IEEE.
Source Title: 2013 1st IEEE Conference on Technologies for Sustainability, SusTech 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/128485
ISBN: 9781467346306
DOI: 10.1109/SusTech.2013.6617293
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