METEOROLOJİK VERİLER KULLANILARAK GÜNEŞ IŞINIM TAHMİNİNDE YAPAY SİNİR AĞLARI PARAMETRELERİNİN DEĞERLENDİRİLMESİ

Doğru ışınım tahmini, fotovoltaik (PV) santralinin verimliliğini arttırarak şebekenin etkin bir şekilde programlanmasına ve güç kalitesinin iyileştirilmesine olanak sağlar. Bu çalışma, güneş enerjisi bakımından verimli bir yer olan Hakkâri ilinde kurulan bir meteoroloji ölçüm istasyonu verileri aracılığıyla küresel güneş ışınım tahmininde yapay sinir ağları (YSA) parametrelerinin potansiyelini göstermektedir. Meteoroloji istasyonundan zaman serisine bağlı olarak ölçülen, rüzgâr hızı, sıcaklık, basınç ve nem parametreleri kullanılarak eş zamanlı gerçekleşen güneş ışınım değerleri YSA modeli oluşturularak tahmin edilmiştir. Oluşturulan model YSA’da yaygın olarak kullanılan çeşitli eğitim algoritmaları ve aktivasyon fonksiyonları ile denenmiş ve en iyi sonuç elde edilmeye çalışılmıştır. Kullanılan modelin performansı istatistiksel göstergeler kullanılarak değerlendirilmiştir. Kullanılan veri seti parametrelerine göre güneş ışınım tahmininde, “trainlm” eğitim algoritması ile “poslin” aktivasyon fonksiyonu kullanılarak oluşturulan model 0,97 regresyon değeri, %1,16 ortalama kare hatası (MSE) ve %0,0881 normalize kök ortalama kare hatası (nRMSE) değeri ile güneş ışınım tahmininde en iyi performansı göstermiştir.

Anahtar Kelimeler:

Güneş ışınım tahmini, güneş enerjisi, Hakkâri, yapay sinir ağları, meteorolojik veriler

EVALUATİON OF ARTIFICIAL NEURAL NETWORK PARAMETERS IN SOLAR RADIATION PREDICTION USING METEOROLOGICAL DATA

Accurate radiation prediction increases photovoltaic (PV) plant efficiency so ensures effective programming of the grid and improvement of power quality. This study demonstrates the prediction potential of artificial neural networks (ANN) parameters in global solar radiation through data from a meteorological measurement station established in Hakkari, Turkey, which is a solar-efficient place. The occurring simultaneous solar radiation values were estimated using the wind speed, temperature, pressure and humidity parameters obtained from the meteorology station depending on the time series, and the relationships between these parameters were modeled using ANN. The created model was tested with various training algorithms and activation functions, and the best result was tried to be obtained. The performance of this model was evaluated using statistical indicators. In prediction of solar radiation according to used data set parameters, the model established by using “trainlm” training algorithm and “poslin” activation function showed the best performance in solar radiation prediction with 0.97 regression value, 1.16% mean square error (MSE) and 0.0881% normalized root mean square error (nRMSE).

Keywords:

Solar radiation prediction, solar energy, Hakkari, artificial neural networks, meteorological data,

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