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Long term wind speed prediction with polynomial autoregressive model

Karakus, Oktay, Kuruoglu, Ercen E. and Altinkaya, Mustafa A. 2015. Long term wind speed prediction with polynomial autoregressive model. Presented at: 23rd Signal Processing and Communications Applications Conference (SIU 2015), 16-19 May 2015. 2015 23nd Signal Processing and Communications Applications Conference (SIU). IEEE, 10.1109/siu.2015.7129907

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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 Çeşme 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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 9781467373869
Last Modified: 07 Feb 2022 11:15

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