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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Prediction of Day Ahead Hourly Solar Radiation by Meteorological Forecasting Supported Artificial Neural Network: A Case Study for Trabzon Province

Meteorolojik Tahmin Destekli Yapay Sinir Ağı ile Gün Öncesi Saatlik Güneş Işınımı Kestirimi: Trabzon ili Örneği

How to cite: Çevik S, Çakmak R, Altaş H. Prediction of day ahead hourly solar radiation by meteorological forecasting supported artificial neural network: a case study for trabzon province. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(2): 87-92. DOI: 10.54856/jiswa.201812029

Full Text: PDF, in Turkish.

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Title: Prediction of Day Ahead Hourly Solar Radiation by Meteorological Forecasting Supported Artificial Neural Network: A Case Study for Trabzon Province

Abstract: Electricity generation from renewable energy sources is increased day by day. Accurate estimation of electricity generation from the renewable energy sources which have intermittent and variable characteristics is a requirement to ensure stable operation of the electrical grid. In this study, a multi-layer artificial neural network (ANN) system, which is supported by meteorological forecasting data, has been proposed to predict day ahead hourly solar radiation. In this context, the ANN system which operates by based on cause-effect relationship has been designed. In order to increase accuracy of the solar radiation prediction of the designed ANN, a similar day selection algorithm has been developed. A unique ANN has been constituted for each season by evaluating the seasons within itself. The designed ANN model has been designed, trained and tested in MATLAB simulation environment without using codes of the MATLAB ANN toolbox. Day ahead hourly solar radiation of Trabzon province has been predicted by the proposed ANN. The accuracy of the predictions has been evaluated by the mean absolute percentage error (MAPE), the root means squared error (RMSE), the mean absolute error (MAE) and the correlation coefficient (r) performance measures.

Keywords: Prediction of solar radiation; artificial neural networks; artificial intelligence; forecasting; Trabzon


Başlık: Meteorolojik Tahmin Destekli Yapay Sinir Ağı ile Gün Öncesi Saatlik Güneş Işınımı Kestirimi: Trabzon ili Örneği

Özet: Yenilenebilir enerji kaynaklarından elektrik üretimi her geçen gün artmaktadır. Elektik şebekesinin kararlı bir şekilde çalışmasını sağlamak için kesintili ve değişken karakteristiğe sahip olan yenilenebilir enerji kaynaklarından yapılacak olan üretimin doğru tahmin edilmesi gerekmektedir. Bu çalışmada gün öncesi saatlik güneş radyasyonunu tahmin etmek için meteorolojik tahmin verileri ile desteklenen çok katmanlı yapay sinir ağı (YSA) sistemi önerilmektedir. Bu bağlamda sebep sonuç ilişkisine dayalı olarak çalışan bir YSA sistemi tasarlanmıştır. Tasarlanan yapay sinir ağının yapacağı güneş ışınımı kestiriminin doğruluğunu arttırmak için benzer gün seçim algoritması geliştirilmiştir. Mevsimleri kendi içerisinde değerlendirerek, her mevsim için farklı bir yapay sinir ağı oluşturulmuştur. Tasarlanan YSA modeli, MATLAB benzetim programında MATLAB’ın YSA araç kutusunda bulunan kodlar kullanılmadan tasarlanmış, eğitilmiş ve test edilmiştir. Önerilen YSA sistemi kullanılarak Trabzon ili için gün öncesi saatlik güneş ışınımı tahmini yapılmıştır. Yapılan tahminlerin doğruluğu, ortalama mutlak yüzde hata (OMHY), karesel ortalama hata (KOH), ortalama mutlak hata (OMH) ve korelasyon katsayısı (r) performans ölçütleri ile değerlendirilmiştir.

Anahtar kelimeler: Güneş ışınımı kestirimi; yapay sinir ağları; yapay zeka; tahmin; Trabzon


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