Adıyaman ve Gaziantep İstasyonlarındaki Güneşlenme Şiddetinin Destek Vektör Makineleri ile Tahmini

Öz

Güneş enerjisi teknolojilerinin kullanımı, birçok ülkede enerji talebini karşılamak ve sürdürülebilir enerji kaynağı sağlamak amacıyla son yıllarda gelişerek artmaktadır. Bu teknolojilerin verimli kullanılabilmesi için, güneşlenme şiddeti verilerinin doğru bir şekilde belirlenmesi gerekmektedir, böylece yapılacak olan yatırımların verimliliği de önceden belirlenebilecektir. Güneş enerjisi ölçüm cihazlarının yetersizliğinin yanında var olan ekipmanların yenilenme veya onarım maliyetlerinden dolayı, literatürde meteorolojik istasyonlardan elde edilen verilerin girdi parametresi olarak kullanılması ve yapay zekâ yöntemleri ile güneşlenme şiddeti verilerinin hesaplanması yapılmaktadır. Bu çalışmada, Adıyaman ve Gaziantep istasyonlarına ait, sıcaklık, nem, ortalama basınç, rüzgâr, aylık açık gün sayısı ve takvim ayı gibi farklı girdi parametreleri kullanılarak, bu istasyonlara ait aylık ortalama güneşlenme şiddeti tahmin edilmeye çalışılmıştır. Aylık ortalama güneşlenme şiddetinin tahmin edilmesi için, destek vektör makineleri yönteminin üç farklı çekirdek fonksiyonu (Radyal, Lineer ve Polinom) kullanılmıştır. Ele alınan çekirdek fonksiyonlarının güneşlenme şiddetini tahmin etmedeki başarısında, belirlilik katsayısı (R2), Karekök Ortalama Karesel Hata (KOKH), Ortalama Mutlak Yüzde Hata (OMYH), Nash–Sutcliffe verimlilik katsayısı (NSE) ve Yüzde Hata (PBIAS) parametreleri başarı kriteri olarak tercih edilmiştir. Çalışma sonucunda, destek vektör makinelerinin Radyal ve Polinom çekirdek fonksiyonlarının güneşlenme şiddetini belirlemede genel olarak başarılı sonuçlar verdiği görülmüştür. Ayrıca, girdi parametresi olarak ortalama sıcaklık ve ortalama basıncın kullanılmasının tahmin modellerinin performansını arttırdığı belirlenmiştir.

Anahtar Kelimeler

• Güneşlenme Şiddeti
• Yapay Zekâ Yöntemleri
• Destek Vektör Makineleri

Estimation of Solar Radiation in Adıyaman and Gaziantep Stations Using Support Vector Machines

Öz

Using of solar energy technologies have been developing and increasing in many countries in recent years to meet the energy demand and to provide sustainable energy source. In order to use the developing technologies efficiently, it is necessary to obtain energy source data, and this will ensure that the investments made in this way are more effective. Due to the insufficiency of solar energy measurement devices and the replacement or repair costs of equipment, it is necessary to use the data obtained from meteorological stations as input parameters in the literature and to calculate the solar adiation data with artificial intelligence methods. In this study, the monthly mean solar radiations of Adıyaman and Gaziantep stations are predicted using different input parameters, such as temperature, humidity, mean pressure, wind, number of clear days in a month, and month number. Three different kernel functions (Gaussian, Linear and Polynomial) of support vector machine are used to estimate the average monthly solar radiation. Coefficient of Determination (R2), Root Mean Square Error (RMSE) Mean Absolute Percentage Error (MAPE) Nash–Sutcliffe model efficiency coefficient (NSE) and Percent Bias (PBIAS) parameters are used to determine the performance of selected kernel functions. As a result of the study, it is seen that the Gaussian and Polynomial kernel functions of support vector machines generally show successful output to determine the solar radiation. In addition, it is determined that the models that used mean temperature and mean pressure as input parameters improve the estimation performance.

Anahtar Kelimeler

• Solar Radiation
• Artificial Intelligence Methods
• Support Vector Machine

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