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Please use this identifier to cite or link to this item: https://doi.org/10.1109/SusTech.2013.6617293
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dc.titleConstrained support vector machines for photovoltaic in-feed prediction
dc.contributor.authorHildmann, M.
dc.contributor.authorRohatgi, A.
dc.contributor.authorAndersson, G.
dc.date.accessioned2016-10-18T06:26:52Z
dc.date.available2016-10-18T06:26:52Z
dc.date.issued2013
dc.identifier.citationHildmann, 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. <a href="https://doi.org/10.1109/SusTech.2013.6617293" target="_blank">https://doi.org/10.1109/SusTech.2013.6617293</a>
dc.identifier.isbn9781467346306
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/128485
dc.description.abstractIn 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/SusTech.2013.6617293
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentENERGY STUDIES INSTITUTE
dc.description.doi10.1109/SusTech.2013.6617293
dc.description.sourcetitle2013 1st IEEE Conference on Technologies for Sustainability, SusTech 2013
dc.description.page23-28
dc.identifier.isiutNOT_IN_WOS
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