Decomposition-Ensemble Learning Approach in Solar Radiation Forecasting

Year 2021, Volume: 11 Issue: 1, 132 - 144, 01.03.2021

Ardan Hüseyin Eşlik , Emre Akarslan , Fatih Onur Hocaoğlu

https://doi.org/10.21597/jist.732025

Cited By: 1

Abstract

The amount of electrical energy to be obtained from solar energy systems varies greatly depending on the solar radiation value. The design and planning of a solar energy system are possible by knowing the radiation value. Since solar radiation intensity has a highly variable structure throughout the day, it is very difficult to capture these changes using a single prediction model. In this context, in recent years, different hybrid models and approaches have been proposed by researchers to overcome the limitations of single models and increase predictive accuracy. In this study, the applicability and performance of the method were investigated by using the Decomposition-Ensemble learning approach, which is a hybrid approach to the estimation of solar radiation intensity data. In addition, it is aimed to increase the time resolution of forwarding solar radiation forecasts. In this context, 15-day solar radiation value was forecasted hourly, using an annual solar radiation data measured hourly with a pyranometer located within Afyon Kocatepe University, Solar and Wind Energy Application and Research Center. In the learning approach, Empirical Mode Decomposition (EMD) method is used for decomposition and Least Squares Support Vector Regression (LS-SVR) method is used for individual predictions. The most appropriate parameter values of LS-SVR models were determined by using a grid search algorithm and 5 fold cross-validation methods. The results obtained from the study showed that the Decomposition-Ensemble learning approach is successful in estimating solar radiation data.

Keywords

Solar radiation forecasting, decomposition-ensemble learning approach, least squares support vector machines (LS-SVM), empirical mode decomposition (EMD)

Güneş Işınımı Tahmininde Ayrıştırma-Birleştirme Öğrenme Yaklaşımı

Abstract

Güneş enerjisi sistemlerinden elde edilecek elektrik enerjisi miktarı büyük oranda güneş ışınım değerine bağlı olarak değişmektedir. Bir güneş enerji sisteminin tasarımı ve planlaması, ışınım değerinin bilinmesi ile mümkündür. Güneş ışınım şiddetinin gün içerisinde yüksek değişkenlik gösteren bir yapıya sahip olması nedeniyle tek bir tahmin modeli kullanılarak bu değişimlerin yakalanması oldukça güçtür. Bu bağlamda, son yıllarda araştırmacılar tarafından tekli modellerin sınırlamalarının üstesinden gelmek ve öngörme hassasiyetini artırmak için farklı hibrit modeller ve yaklaşımlar önerilmiştir. Bu çalışmada, güneş ışınım şiddeti verilerinin tahmininde hibrit bir yaklaşım olan Ayrıştırma-Birleştirme öğrenme yaklaşımı kullanılarak yöntemin uygulanabilirliği ve performansı araştırılmıştır. Ayrıca ileriye yönelik güneş ışınımı tahminlerinin zaman çözünürlüğünün arttırılması amaçlanmıştır. Bu kapsamda Afyon Kocatepe Üniversitesi, Güneş ve Rüzgâr Enerjisi Uygulama ve Araştırma Merkezi bünyesinde yer alan bir piranometre ile saatlik olarak ölçülmüş bir yıllık güneş ışınım verisi kullanılarak 15 günlük güneş ışınımı değeri saatlik olarak tahmin edilmiştir. Öğrenme yaklaşımında ayrıştırma işlemi için Ampirik Kip Ayrışımı (AKA), bireysel tahminler için ise En Küçük Kareler Destek Vektör Regresyon (EKK-DVR) yöntemleri kullanılmıştır. EKK-DVR modellerinin en uygun parametre değerleri grid arama algoritması ve 5 katlamalı çapraz doğrulama yöntemleri kullanılarak belirlenmiştir. Çalışmadan elde edilen sonuçlar Ayrıştırma-Birleştirme öğrenme yaklaşımının güneş ışınım verilerinin tahmininde başarılı olduğunu göstermiştir.

Keywords

Güneş ışınımı tahmini, ayrıştırma-birleştirme öğrenme yaklaşımı, ampirik kip ayrışımı (AKA), en küçük kareler destek vektör makineleri (EKK-DVR)

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