TY  - JOUR
T1  - Modeling of Monthly Mean Solar Energy Potential using Artificial Neural Network
TT  - Yapay Sinir Ağı Kullanılarak Aylık Ortalama Güneş Enerjisi Potansiyelinin Modellenmesi
AU  - Timoçin, Erdinç
PY  - 2025
DA  - August
Y2  - 2025
DO  - 10.53433/yyufbed.1665961
JF  - Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi
JO  - YYUFBED
PB  - Van Yüzüncü Yıl Üniversitesi
WT  - DergiPark
SN  - 1300-5413
SP  - 512
EP  - 523
VL  - 30
IS  - 2
LA  - en
AB  - The aim of this study is to develop an artificial neural network (ANN) model for accurately predicting monthly mean solar radiation and irradiance for Mersin (36.8o N, 34.6o E, Türkiye). The prediction of monthly mean solar radiation and irradiance was made by using two different ANN (NN-1 and NN-2) models with different input parameters and thus, a dual solution strategy for the monthly mean solar radiation and irradiance forecasts was presented. The ANN models were trained for the target parameters (monthly mean solar radiation and irradiance) at each month of the year. The training, testing and validating for both models were conducted using the data obtained for the period from 2004 to 2024. The performance results of these alternative models compared with each other. The accuracy of the models to predict the monthly mean solar radiation and irradiance are identified based on root mean square errors (RMSE) and cross-correlation coefficients (R). The NN-2 model has smaller RMSE values and has bigger R values. That is, the NN-2 model has higher prediction success with lower prediction error for both monthly mean solar radiation and irradiance intensity. The presence of two models may be advantageous for more precise forecasting situations and the NN-2 model can be chosen for such cases. In addition, the application of the NN-2 model proposed in this study can be extended to other locations.
KW  - Artificial neural network
KW  - Modelling
KW  - Solar irradiance
KW  - Solar radiation
N2  - Bu çalışmanın amacı, Mersin (36.8o N, 34.6o E, Türkiye) için aylık ortalama güneş radyasyonu ve ışınım şiddetini doğru bir şekilde tahmin etmek için bir yapay sinir ağı (YSA) modeli geliştirmektir. Aylık ortalama güneş radyasyonu ve ışınım şiddetinin tahmini, farklı giriş parametrelerine sahip iki farklı YSA (NN-1 ve NN-2) modeli kullanılarak yapılmış ve böylece aylık ortalama güneş radyasyonu ve ışınım şiddeti tahminleri için ikili bir çözüm stratejisi sunulmuştur. YSA modelleri, yılın her ayında hedef parametreler (aylık ortalama güneş radyasyonu ve ışınım şiddeti) için eğitilmiştir. Her iki model için eğitim, test ve doğrulama işlemleri 2004-2024 yılları arasında elde edilen veriler kullanılarak gerçekleştirilmiştir. Birbirine alternatif olan bu modellerin performans sonuçları birbirleriyle karşılaştırılmıştır. Aylık ortalama güneş radyasyonu ve ışınım şiddetini tahmin eden modellerin doğruluğu, kök ortalama karekök hatalarına (RMSE) ve çapraz korelasyon katsayılarına (R) dayanarak belirlenmiştir. NN-2 modeli daha küçük RMSE değerlerine ve daha büyük R değerlerine sahiptir. Yani, NN-2 modeli hem aylık ortalama güneş radyasyonu hem de ışınım yoğunluğu için daha düşük tahmin hatasıyla daha yüksek tahmin başarısına sahiptir. İki modelin varlığı daha hassas tahmin durumları için avantajlı olabilir ve bu gibi durumlar için NN-2 modeli seçilebilir. Ayrıca, bu çalışmada önerilen NN-2 modelinin uygulaması diğer konumlara genişletilebilir.
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UR  - https://doi.org/10.53433/yyufbed.1665961
L1  - https://dergipark.org.tr/tr/download/article-file/4727142
ER  -