Neural Prediction of Wind Blowing Durations Based on Average Wind Speeds for Akhisar Location

Abstract

Renewable energy resources are widely preferred over conventional resources as they are environmentally favorable. Wind energy is one of the important renewable energy resources and has been widely developed recently. The energy produced from wind is dependent upon several factors. One of them is average wind speed and the other is wind blowing period. In this study, the wind blowing period is estimated based on annual average wind speed, Hellman coefficient and tower height using artificial neural networks (ANN). The results of ANN are compared with a conventional method in which Rayleigh distribution is employed.

Keywords

• Wind energy, Wind blowing time, Hellmann coefficient, Artificial neural networks.

Akhisar Bölgesi için Ortalama Rüzgar Hızlarına Bağlı Rüzgar Esme Sürelerinin Yapay Sinir Ağları ile Tahmini

Öz

Günümüzde enerjinin temiz, yerli ve yenilenebilir olması sadece ülkemizde değil tüm dünya ülkelerinde çoğunlukla kabul görmektedir. Alternatif ve temiz olan bu enerji kaynaklarından biri ve en önemlisi de rüzgar enerjisidir. Atmosferi kirleten fosil yakıtlarla karşılaştırıldığında rüzgar enerjisini elektrik enerjisine dönüştüren sistemlerin hızlı bir şekilde geliştiği ve kullanıldığı görülmektedir. Rüzgar türbinlerinden elde edilen elektrik enerjisi birkaç faktöre bağlı olarak değişir. Bu faktörlerden ikisi ortalama rüzgar hızı ve rüzgar esme süreleridir. Bu çalışmada, Akhisar bölgesi için yıllık ortalama rüzgar hızı, Hellmann katsayısı, kule yüksekliği gibi parametrelere bağlı rüzgar esme süreleri Yapay Sinir Ağları (YSA) ile analiz edilmektedir. Rüzgar esme süreleri analizinde Rayleigh dağılımı'nın kullanıldığı geleneksel yöntem(GY) ile YSA'nın karşılaştırılması yapılmaktadır.

Anahtar Kelimeler

• Rüzgar enerjisi, Rüzgar esme süresi, Hellmann katsayısı, Yapay sinir ağları.

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