Sema ARİMAN, Gaye Yeşim TAFLAN, Esra ÇELİK

Samsun Bölgesi için Güneş RadyasyonununYapay Sinir Ağı ile Tahmini

Yenilenebilir enerji kaynaklarından güneş enerji sistemlerinin tasarımında güneş radyasyonunu tahmin etmek oldukça önemlidir. Güneş enerjisi, global güneş radyasyonuna bağlıdır. Güneş radyasyonunun Yapay Sinir ağı modeli (YSA) ile tahmin edilmesinde girdideğişkeni olarak, hava sıcaklığı, güneşlenme süresi, buhar basıncı, bulutluluk gibi çeşitli meteorolojik değişkenler kullanılmaktadır. Buçalışmada, Samsun bölgesi için Mart 2017-Şubat 2019 tarihleri arasındaki günlük güneş radyasyonu YSA yöntemi ile tahmin edilmeyeçalışılmıştır. YSA yönteminde farklı giriş değişkenleri için Levenberg-Marquardt eğitim algoritması, logaritmik sigmoid ve doğrusaltransfer fonksiyonu kullanılmıştır. Model performansı en yüksek 9 girişli meterolojik veriler (ortalama sıcaklık, ortalama nispi nem,ortalama rüzgar hızı, ortalama buhar basıncı, ortalama bulutluluk oranı, güneşlenme süresi, maksimum sıcaklık, minimum sıcaklık, 5cm’de toprak sıcaklığı) ile elde edilmiştir. Test verileri için R değeri 0.9603, MSE değeri 0.3516 olarak bulunmuştur. İleri beslemeliYSA modeli yaklaşımının diğer meteorolojik değişkenler ile birlikte güneş radyasyonunu tahmin etmek için yüksek bir performanssağladığı görülmüştür. Ayrıca, YSA’ya güneşlenme süresi giriş olarak verildiğinde, R değeri 0.9032 olarak elde edilmiştir.

Prediction of Solar Radiation with Artificial Neural Network for Samsun Region

It is very important to predict solar radiation in the design of solar energy systems from renewable energy sources. Solar energy dependson global solar radiation. Various meteorological variables such as air temperature, sunshine duration, vapor pressure, cloudiness areused as input variables in estimating solar radiation with Artificial Neural Network Model (ANN). In this study, ANN was used topredict the daily solar radiation values of Samsun region between March 2017 and February 2019. Levenberg-Marquardt trainingalgorithm, logarithmic sigmoid and linear transfer function were used for different input parameters in ANN method. The best modelperformance was obtained with 9-input meteorological data (average temperature, average wind speed, average vapor pressure, averagecloudiness rate, sunshine duration, maximum temperature, minimum temperature, soil temperature at 5 cm). The correlation coefficient(R) for the test data was 0.9603 and the mean square error (MSE) was 0.3516. It has been observed that the feed forward ANN modelprovides a high performance for predicting solar radiation along with other meteorological parameters. In addition, when the sunshineduration was given as input to the ANN, R value was obtained as 0.9032.

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