In this paper, we propose a computational scheme for the problem of wind power forecasting. Such scheme combines a local weighted regression model with fuzzy transform. The latter provides a way to reduce the cardinality of the learning problem, resulting in a more efficient algorithm. Numerical examples show the effectiveness of the proposed approach.
Joining fuzzy transform and local learning for wind power forecasting
Loia, Vincenzo
Membro del Collaboration Group
;Tomasiello, StefaniaMembro del Collaboration Group
;Vaccaro, AlfredoMembro del Collaboration Group
2017
Abstract
In this paper, we propose a computational scheme for the problem of wind power forecasting. Such scheme combines a local weighted regression model with fuzzy transform. The latter provides a way to reduce the cardinality of the learning problem, resulting in a more efficient algorithm. Numerical examples show the effectiveness of the proposed approach.File in questo prodotto:
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