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
2017-01-01
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:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
