Güneydoğu Anadolu bölgesi için global güneş ışımasının ve güneşlenme süresinin istatiksel metodlar ile tahmin edilmesi ve karşılaştırılması

Abstract

Günümüzde, gelişen teknolojilere paralel olarak enerjiye olan talep de artmaktadır. Artan bu enerji taleplerinin karşılanması konusunda bilim insanları yeni alternatif enerji kaynakları geliştirmeye çalışmaktadırlar. Bu çalışmaların büyük bir bölümü ise fosil yakıtlara alternatif olabilecek yenilenebilir enerji kaynakları üzerinedir. Yenilenebilir enerji kaynaklarından olan rüzgâr ve güneş enerjisi coğrafik koşullara bağlı olarak, bölgelere göre değişiklik arz etmektedir. Özellikle Türkiye’de güneydoğu Anadolu Bölgesi, Türkiye ortalaması olan 1400 kWh/m2-yıl’dan daha yüksek, bir güneş enerji potansiyeline sahiptir. Bu makalede, Güneydoğu Anadolu Bölgesi için, güneş enerjisinin elektrik enerjisine dönüşümünde önemli bir parametre olan, global güneş ışınımı (GGI) ve güneşlenme sürelerinin (GS) tahmininde, iki farklı istatiksel yöntem ilk kez kullanılmıştır. Kullanılan yöntemler, üstel ağırlıklı hareketli ortalama (ÜAHO) ve üstel ağırlıklı hareketli ortalama bazlı gaussian dağılımı(ÜAHOG)’dır. Yapılan çalışmada, global güneş ışınımı ve güneşlenme süresinin iki farklı istatistiksel yöntemle tahmin edilmesi ve bu yöntemlerin birbirleriyle olan karşılaştırılması sunulmuştur. Ayrıca yöntemlerin başarı oranı, belirleme katsayısı R2 ve ortalama mutlak yüzdelik hata(OMYH) kullanılarak test edilmiştir. Yapılan hesaplamalar sonucunda Hem GGI ve GS için ÜHAO ve ÜAHOG kullanılarak hesaplanan R2 değerleri 1’e yakın olarak bulunurken, diğer taraftan ÜHAO ve ÜAHOG kullanılarak hesaplanan OMYH değerleri yöntemlerin mükemmel tahmin oranına sahip olduğunu gösteren 0-10(kWh/m2-gün) aralığında hesaplanmıştır. Elde edilen sonuçlar GGI ve GS tahmini için kullanılan her iki yöntemin de uygun olduğunu göstermiştir.

Keywords

• Global güneş ışınımı, güneşlenme süresi, Üstel Ağırlıklı Hareketli Ortalama (ÜAHO), Gaussian dağılımı

To predict and compare global solar radiation and sunshine duration by using statistical methods for southeastern region of Turkey

Abstract

Dependent of development in technology it causes to increase in demand of energy. This problem is a dilemma for science and industry. To overcome this situation, scientist and researcher try to improve new solution for energy problem. Solar energy that is a crucial part of renewable energy is in the interest of scientists. Especially for regions in the world which has a high potential of solar energy, it is alternative solution to fossil fuel. Southeastern Anatolian region of turkey has rich in view of solar energy. Due to advantage of this region solar energy may be replaced to conventional energy types. Because countries such Turkey that has in progress of development has to solve their energy problem by various methods. Although that region of Turkey has a high potential, it is not enough to benefit from solar energy. The aim is this paper to contribute for scientist, researchers and investigators by eliminating the disadvantages such as lack of solar parameters measurement stations due to geographical and economic problems in that field. The meteorological measurement station in Turkey are dependent to Meteorological service of Turkish state. In addition some station are established in University for scientific researches. That region of Turkey there is no enough measurement station. The contribution proposes in that paper tries to predict solar parameters by using earlier years’ data. The important parameters of solar energy are sunshine duration and global solar radiation. These parameters are used in conversion of solar energy to electrical energy. This paper proposes two statistical method which are used earlier year solar data to predict next years’ solar parameters such as global solar radiation and sunshine duration. First method is exponentially weighted moving average (EWMA). This method needs minimum two years’ data for short or long term prediction. Second method is called as exponentially weighted moving average (EWMA) based Gaussian distribution method. It needs also two years’ solar data for prediction. Both mentioned method are applied to five cites of southeaster Anatolia region of Turkey that are Gaziantep, Şanlıurfa, Diyarbakır, Batman and Mardin. The two years’ (1998-2000) data are used for test class years’ data in Gaziantep, Şanlıurfa, Diyarbakır, Batman but in Mardin two years’ data used for test class years are between 2012-2013. The predicted years for Cities of Gaziantep, Şanlıurfa are 10 years, for Diyarbakır are 8 years, for Batman are 6 years which are long term prediction. However for Mardin cities predicted years are 2 years and it is called as short term prediction. It is a obligation to selected this years for prediction because only that years solar data can be obtained from metrological service of Turkish state. For both method two statistical tools which area mean average percentage error (MAPE) and determination coefficient (R2 ). The computed MAPE for global solar radiation and sunshine duration in EWMA method is between 0-10(kWh/m2 ) that is excellent prediction accuracy and the computed R2 for global solar radiation and sunshine duration is close to 1 which is indicated measured and predicted data are similar. The computed values of MAPE for EWMA based Guassian distribution method is also in acceptable range that is between 0-10(kWh/m2) for global solar radiation. On the other hands they are also indicated excellent prediction accuracy for sunshine duration excluding Diyarbakır and Batman cities it is indicated good prediction accuracy which is in the interval of 10-20(kWh/m2). The computed values of R2 for both global solar radition and sunshine duration in EWMA based Gaussian distribution method is close to 1 and it means the predicted data are fitted with measured data. As a result EWMA method and EWMA based Gaussian distribution method have high prediction accuracy. However the result of EWMA methods has a higher prediction accuracy than values of EWMA based Gaussian distribution method.

Keywords

• Global solar radiation, sunshine duration, EWMA, Gaussian distribution