Güneş Işınım Tahmini için Farklı Güneşlenme Durumlarından Faydalanan Hibrit Bir Yöntem Tasarımı

Güneş enerjisinin sürekli genişlemesi, radyasyonun doğru tahminini önemli bir konu haline getirmiştir. Güneş enerjisi üretiminin doğru bir tahmini, fotovoltaik (PV) ve rüzgar jeneratörlerinin akıllı şebekelere etkin entegrasyonu için çok önemlidir. Güneş enerjisinin kesintili doğası, yenilenebilir enerji sistemi operatörleri için operasyonel planlama ve zamanlama açısından birçok zorluk teşkil etmektedir. Bu nedenle güneş ışınımının hibrit yöntemlerle tahmin edilmesi yaygınlaşmaktadır. Bu yazıda, güneş radyasyonunu tahmin etmek için bir hibrit yöntem önerilmiş olup, burada tahmin modeli açıklık indeksine dayalı olarak belirlenir. Çalışmada, Mardin ilinin Türkiye Meteoroloji Genel Müdürlüğünden (TMGM) elde edilen iki yıllık güneş radyasyonu verileri kullanılmıştır. Tahmin edici olarak YSA, NARX ağları ve Ridge regresyon yöntemleri kullanılmış ve çalışmanın ilk aşamasında eğitim verileri her üç yaklaşımla da modellenmiştir. Bulutluluk indeksi için, az bulutlu, bulutlu ve çok bulutluya karşılık gelecek şekilde üç aralık belirlenmiştir. Tahmin edici olarak kullanılan üç yöntem ile eğitim verisi modellenmiş ve her bir yöntemin belirlenen her bir bulutluluk indeksi aralığındaki başarısı incelenmiştir. Sonuç olarak, hibrit tahmin algoritmasında, önce yapay sinir ağları kullanılarak açıklık indeksi tahmin edilir ve daha sonra tahmin edilen açıklık indeksi aralığında en başarılı model kullanılarak gelecekteki güneş radyasyonu değeri tahmin edilir. Deneysel sonuçlar, önerilen hibrit yöntem ile modellerin bireysel olarak kullanıldığı duruma göre daha başarılı tahminler yapıldığını göstermektedir.

Design of a Hybrid Method Exploiting Different Insolation States for Solar Radiation Forecasting

The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. An accurate prediction of solar energy production is crucial for the effective integration of photovoltaic (PV) and wind generators in smart grids. The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. For this reason, forecasting solar radiation by means of the hybrid methods is becoming widespread. In this paper, a hybrid method for predicting solar radiation is proposed, wherein the prediction model is determined based on the clearness index. The study used two-year solar radiation data of the province of Mardin obtained from the Turkish State Meteorological Service (TSMS). As predictors, ANN, NARX networks, and Ridge regression methods were used, and the training data were modeled with all three approaches in the first stage of the study. The clearness index was determined into three ranges; slightly cloudy, cloudy, and mostly cloudy. The training data were modeled with three methods used as estimators, and the success of each method was examined in each defined clearness index range. As a result, in the hybrid prediction algorithm, the clearness index is first estimated using artificial neural networks, and then the future solar radiation value is predicted by using the most successful model within the predicted clearness index range. Experimental results show that more successful predictions are made with the proposed hybrid method than when models are used individually.

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