Istituto di Scienza e Tecnologie dell'Informazione     
Karakus O., Kuruoglu E. E., Altinkaya M. A. Long term wind speed prediction with polynomial autoregressive model. In: SIU 2015 - 23rd Signal Processing and Communications Applications Conference (Malatya, Turkey, 16 May 2015). Proceedings, pp. 645 - 648. IEEE, 2015.
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 ネ群me 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.
Rzgar enerjisi rzgar覺n yenilenebilir bir kaynak olmas覺 nedeniyle enerji retme y霵temleri aras覺nda tercih edilen bir y霵temdir. Belirli bir peryotta rzgar h覺z覺n覺n 霵g顤lmesi, rzgar h覺z覺n覺n rzgar gcyle birebir bag覺nt覺s覺 nedeniyle byk 霵em arz etmektedir. Rzgar h覺z覺 verisinin dogrusal olmayan karakteri nedeniyle, 12 saatten uzun 霵g顤leri i蓾n dogrusal olmayan modellerin, 驆baglan覺ml覺 (Autoregressive-AR), 驆baglan覺ml覺 yryen ortalama (Autoregressive Moving Average - ARMA) gibi dogrusal zaman serisi y霵temlerine g顤e daha basar覺l覺 sonu蔮ar verdigi bilinmektedir. Bu bildiride, ネsme yar覺madas覺nda ge蔂is rzgar h覺z覺 闤踙mleri kullan覺larak 48-saat ileri rzgar h覺z覺n覺n 霵g顤lmesi i蓾n bir y霵tem 霵erilmektedir. 琄erilen y霵temde, kullan覺lan rzgar h覺z覺 verisi, Polinom AR (Polynomial AR - PAR) bir model ile modellenmistir. Modellere ait katsay覺lar dogrusal en k踙k kareler (Least Squares - LS) y霵temi ile kestirilmi群tir ve 48-saat ileri rzgar h覺z覺 霵g顤s farkl覺 modeller i蓾n ger蔒klestirilmistir. Sonu olarak, PAR modeli ile modellenen rzgar h覺z覺n覺n, 12 saatin zerindeki 霵g顤lerinde, AR ve ARMA modele g顤e daha basar覺l覺 oldugu g驆lemlenmistir.
URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7129907
DOI: 10.1109/SIU.2015.7129907
Subject Wind speed prediction
Polynomial autoregressive process
Nonlinear stochastic process
J.2 PHYSICAL SCIENCES AND ENGINEERING. Earth and atmospheric sciences
G.3 PROBABILITY AND STATISTICS. Time series analysis
G.3 PROBABILITY AND STATISTICS. Correlation and regression analysis
86-08 Computational methods
62M20 Prediction
62M10 Time series, auto-correlation, regression, etc.
62J02 General nonlinear regression

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