Rüzgar Şiddetinin Yapay Sinir Ağları Yöntemleri ile Modellenmesi

Abstract

Rüzgar enerjisi, güvenilir ve uygun maliyetli elektrik sağlama kapasitesi nedeniyle önde gelen yenilenebilir enerji kaynakları arasında yer almaktadır. Rüzgar enerjisi dönüşüm sistemlerinin karmaşıklığı, ileriye dönük analizlere dayalı yeni tekniklerin geliştirilmesini zorunlu kılmaktadır. Bu çalışmada, Yapay Sinir Ağları (YSA) yöntemi kullanarak Çanakkale İli Baba Burnu bölgesinde 2001, 2002 ve 2003 yıllarına ait rüzgar enerji potansiyelinin hesaplanmasına yönelik, rüzgar şiddeti tahmini ile ilgili bir çalışma yapılmış ve gelecekte o bölgede rüzgar enerjisi üretimine yönelik ön bilgi elde edilmiştir. Araştırma çalışması sonucunda, YSA model çıktıları ile gözlenen rüzgar şiddeti değerleri arasındaki ilişki katsayısının %91 olduğu saptanmıştır. Modelin başarısı irdelenmiş, 10 m yükseklikte rüzgar şiddeti tahmini ile ilgili 1,905m/s, karekök hata (RMSE) ve 1.38m/s, ortalama karekök hata (RMSEA) 0,07 olarak hesaplanmıştır. Araştırma sonucunda, RMSEA değerlerinin 0.05 ile 0.08 arasında olması gözlem ve model sonuçları arasında yeterli bir uyum olduğunu göstermektedir.

Keywords

• : Rüzgar enerjisi
• Yapay sinir ağları
• karekök hata
• ortalama karekök hata

Modeling of Wind Intensity with Artificial Neural Networks Methods

Abstract

Wind energy is among the leading renewable energy sources due to its capacity to provide reliable and cost-effective electricity. The complexity of wind energy conversion systems necessitates the development of new techniques based on prospective analysis. In this study, using Artificial Neural Networks (ANN), a study was conducted to calculate the wind energy potential of Çanakkale Province Baba Burnu region for the years 2001, 2002 and 2003, and preliminary information about wind energy production in that region in the future was obtained. As a result of the research study, it was determined that the correlation coefficient between the ANN model outputs and the observed wind speed values was 91%. The success of the model was examined, and it was calculated as 1.905m/s, root mean-square error (RMSE) and 1.38m/s, Root Mean Square Error of Approximation (RMSEA) of 0.07 for the wind force estimation at 10 m height. shows that there is sufficient agreement between the model results

Keywords

• Wind energy
• Artificial Neural Network
• root square error
• root mean square error of approximation

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