Prediction of Photovoltaic Panel Power Outputs using Time Series and Artificial Neural Network Methods

Öz

Solar energy is one of the renewable energy sources that has been in high demand in the last decades. With the increasing penetration of photovoltaic (PV) systems in around the world, accurate estimation of the power output of PV systems has become an important issue. Since PV systems directly convert sunlight into electrical energy, PV power output varies depending on environmental conditions. In order to deal with the periodic and non-stationary problems of PV output power, modelling methods are widely use for forecasting. The main purpose of this study is to lead an assessment of forecasting of the PV power outputs in short-time. For this purpose, data are obtained from experimental activities carried out on a real 250 kWp PV stystem, which is located in T.C Tekirdağ Namık Kemal University, Süleymanpaşa district of Tekirdağ province. All parametres are measured hourly with three times according to inclination of the panel setups (0˚, 30˚,60˚). In this sense, this study differs from the previously studies in literature, as it expands the forecasting model with considering of different panel angle. In the first stage, the significant variables for predicting PV power output are identified based on both correlation analysis and stepwise regression analysis. The findings are shown that solar radiation and angle of inclination of the panel are significant predictors of the generation of PV power. In the second stage, three different model are proposed based on Time Series Analysis (TSA) and Artificial Neural Network (ANN) approaches in order to predict power production of PV system. Furthermore, the accuracies of the models are analyzed in order to better understand the intrinsic errors caused and to evaluate its potential in energy forecasting applications. All models are compared in terms of the correlation coefficient (R), coefficient of determination (R2), mean absolute percentage error (MAPE). The results of analyses show that the ANN models have higher accuracy than the TSA model for forecasting PV power.

Anahtar Kelimeler

• Artificial neural network
• Back propagation
• PV power forecasting
• ARIMA
• Tekirdağ

Prediction of Photovoltaic Panel Power Outputs using Time Series and Artificial Neural Network Methods

Öz

Güneş enerjisi, son yıllarda yüksek talep gören yenilenebilir enerji kaynaklarından biridir. Fotovoltaik (FV) sistemlerin dünya çapında artan yaygınlığıyla birlikte, FV sistemleri güç çıkışının doğru tahmini önemli bir konu haline gelmiştir. FV sistemleri doğrudan güneş ışığını elektrik enerjisine dönüştürdüğünden, FV güç çıkışı çevre koşullarına bağlı olarak değişkenlik gösterir. FV çıkış gücünün periyodik olma ve durağan olmama sorunlarının üstesinden gelebilmek amacı ile yapılan tahminlemelerde modelleme yöntemleri yaygın olarak kullanılmaktadır. Bu çalışmanın temel amacı, kısa süreli FV güç çıkışı tahminlerinin değerlendirilmesinde yol gösterici olmaktır. Bu amaçla toplanan veriler, Tekirdağ ili Süleymanpaşa ilçesine bağlı T.C Tekirdağ Namık Kemal Üniversitesi'nde kurulan bir 250 kWp’lık FV sistemi ile gerçekleştirilen deneysel faaliyetlerden elde edilmiştir. Tüm parametreler, saat bazında farklı panel eğim açıları (0˚, 30˚, 60˚) dikkate alınarak üçer kez ölçülmüştür. Bu anlamda, bu çalışma tahmin modelini farklı panel açılarını da dikkate alarak genişletmesi nedeniyle literatürdeki önceki çalışmalardan farklılık göstermektedir. İlk aşamada, FV güç çıktısını tahmin etmede kullanılacak anlamlı değişkenler hem korelasyon analizi hem de aşamalı regresyon analizi sonuçlarına göre belirlenmiştir. Bulgular, güneş radyasyonunun ve panel eğim açısının, FV gücü üretiminin önemli belirleyicileri olduğunu göstermiştir. İkinci aşamada, FV sisteminin güç üretimini tahmin etmek için Zaman Serisi Analizi (TSA) ve Yapay Sinir Ağı (YSA) yaklaşımlarına dayalı olarak üç farklı model önerilmiştir. Ayrıca, enerji tahmin uygulamalarında ortaya çıkan içsel hataları daha iyi anlamak ve potansiyelini değerlendirmek için modellerin doğrulukları analiz edilmiştir. Tüm modeller korelasyon katsayısı (R), belirleme katsayısı (R2), ortalama mutlak yüzde hatası (MAPE) açısından karşılaştırılmıştır. Analiz sonuçları, FV gücünü tahmin etmek için YSA modellerinin TSA modelinden daha yüksek doğruluğa sahip olduğunu göstermektedir.

Anahtar Kelimeler

• Yapay sinir ağı
• Geri yayılım
• ARIMA
• Tekirdağ
• FV güç tahmini

Kaynakça

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