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 | 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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