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Title: Long term wind speed prediction with polynomial autoregressive model
Authors: Karakuş, Oktay
Kuruoğlu, Ercan E.
Altınkaya, Mustafa Aziz
Keywords: AR
nonlinear time series
long term wind speed prediction
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: Wind energy is one of the preferred energy generation methods because wind is an important renewable energy source. Prediction of wind speed in a time period, is important due to the one-to-one relationship between wind speed and wind power. Due to the nonlinear character of the wind speed data, nonlinear methods are known to produce better results compared to linear time series methods like Autoregressive (AR), Autoregressive Moving Average (ARMA) in predicting in a period longer than 12 hours. A method is proposed to apply a 48-hour ahead wind speed prediction by using the past wind speed measurements of the (Cesme Peninsula. We proposed to model wind speed data with a Polynomial AR (PAR) model. Coefficients of the models are estimated via linear Least Squares (LS) method and up to 48 hours ahead wind speed prediction is calculated for different models. In conclusion, a better performance is observed for higher than 12-hour ahead wind speed predictions of wind speed data which is modelled with PAR model, than AR and ARMA models.
Description: 23nd Signal Processing and Communications Applications Conference (SIU)
ISBN: 978-1-4673-7386-9
ISSN: 2165-0608
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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