Ayrıştırma Yöntemlerinin Derin Öğrenme Algoritması ile Tanımlanan Rüzgâr Hızı Tahmin Modeli Başarımına Etkisinin İncelenmesi

Öz

Son on yılda, rüzgâr enerjisine dayalı yenilenebilir enerji kaynaklarının kullanımındaki kayda değer artış, bu kaynakların ihtiyaçlara kesintisiz ve tahmin edilebilir bir şekilde cevap verebilmesini sağlamak için rüzgâr hızı tahmin çalışmalarının önemini arttırmaktadır. Rüzgâr enerjisinden teknolojik olarak faydalanmak için; yararlanma imkânlarının bilinmesi, yüksek rüzgâr enerjisi potansiyeline sahip bölgelerin belirlenmesi, rüzgâr karakteristiklerinin ve hızlarının tahmin edilebilir olması oldukça önemlidir. Güvenilir ve yüksek hassasiyetli rüzgâr hızı tahmini, rüzgâr gücünün verimli kullanımı ve kullanılması açısından hayati önem arz etmektedir. Rüzgâr hızının durağan olmaması ve stokastik yapısı, rüzgâr hızı tahmininde ayrıştırma yöntemlerini ön plana çıkarmaktadır. Bu çalışmada, ayrıştırma yöntemlerinden ampirik kip ayrışımı, topluluk ampirik kip ayrışımı ve ampirik dalgacık dönüşümünün derin öğrenme yöntemlerinden uzun-kısa süreli bellek ile elde edilen rüzgar hızı tahmin modeli başarımına etkisi incelenmektedir. Türkiye'nin Marmara bölgesindeki üç rüzgâr istasyonundan toplanan veriler her bir ayrıştırma yöntemi ile alt bant sinyallerine ayrıştırılarak uzun-kısa süreli bellek model yapısı ile kombine rüzgâr hızı tahmin modeli oluşturulmaktadır. Her bir ayrıştırma yöntemi ile birlikte elde edilen kombine modellerin başarımları istatistiksel hata ölçütlerine göre değerlendirilmekte ve rüzgâr hızı tahmin modeli başarımına etkisi en yüksek ayrıştırma yöntemi, melez rüzgâr hızı tahmin modeli elde edilmesi çalışmalarında önerilmektedir.

Anahtar Kelimeler

• Rüzgâr hızı
• Tahmin modeli
• Ayrıştırma yöntemleri
• Derin öğrenme
• Uzun-Kısa Süreli Bellek
• Ampirik Dalgacık Dönüşümü

Investigation of the Effect of Decomposition Methods on Wind Speed Forecasting Model Performance Defined by Deep Learning Algorithm

Abstract

In the last decade, the significant increase in the use of renewable energy sources based on wind energy has increased the importance of wind speed forecasting studies to ensure that these resources can respond to the needs in an uninterrupted and predictable manner. In order to be able to utility from wind energy technologically, it is very important to knowing the facilities of utilization, determining the regions, which have high potential of wind energy, being predictable the wind characteristics and speeds. The reliable and high accuracy wind speed forecasting is of vital to the efficient exploitation and utilization of wind power. The non-stationary and stochastic structure of the wind speed raise to the forefront the decomposition methods in wind speed forecasting. In this study, the effect of empirical mode decomposition, ensemble empirical mode decomposition and empirical wavelet transform on the performance of wind speed forecasting model obtained with long-short term memory from deep learning methods is investigated. The data collected from five wind farms in Marmara region, Turkey are decomposed to subband signal by these three decomposition methods, and the combined wind speed forecasting model is obtained with the long-short-term memory model structure. The performance of the combined models obtained by each decomposition method has been evaluated according to the statistical error criteria, and the decomposition method that is the highest effective to performance of wind speed forecasting model is suggested for the studies of obtaining the hybrid wind speed forecasting model.

Keywords

• Wind speed
• Forecasting model
• Decomposition methods
• Deep learning
• Long-Short Term Memory
• Empirical Wavelet Transform

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