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|Title:||Constrained support vector machines for photovoltaic in-feed prediction||Authors:||Hildmann, M.
|Issue Date:||2013||Citation:||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|
|Appears in Collections:||Staff Publications|
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