Bayes Optimizasyonu Ve Topluluk Öğrenmesine Dayalı Kısa Dönem Rüzgar Gücü Tahmin Yaklaşımı

Year 2021, Volume: 4 Issue: 2, 142 - 154, 23.09.2021

Kübra Yazıcı , Semra Boran

https://doi.org/10.38016/jista.889991

Cited By: 1

Abstract

Rüzgar enerjisi çalışmalarında, rüzgâr santralleri için kısa dönem enerji üretim miktarının tahmini ve piyasaya verilecek üretim teklifinin belirlenmesi önemli bir rol oynamaktadır. Çalışmada Türkiye’de bulunan ve kurulu gücü 3600 kW olan rüzgar türbinin saatlik kısa dönem rüzgar enerjisi tahmini yapılmıştır. Tahmin sonuçları mevsimsel ve yıllık olarak değerlendirilmiştir. Kısa dönem rüzgar gücü tahmini için bayes optimizasyonu, destek vektör regresyonu, gauss süreç regresyonu, karar ağacı, stacking ve bagging algoritmalarının birleşiminden oluşan yeni hibrit modeller geliştirilmiştir. Önerilen tahmin yaklaşımında farklı regresyon algoritmaları ile bayes optimizasyon yöntemi ve topluluk algoritmaları birleştirilerek tahmin hatalarının azaltılması amaçlanmıştır. Çalışmada temel öğrenen olarak seçilen regresyon algoritmalarının hiper parametre seçiminde diğer rüzgar tahmin çalışmalarından farklı olarak ilk defa bayes optimizasyonu kullanılmıştır. Temel öğreniciler içerisinde en düşük hata değerlerine sahip bayes algoritması ile optimize edilmiş karar ağacı ve gauss süreç regresyonu, torbalama ve istifleme ile birleştirilmiştir. Topluluk öğrenmesi algoritmalarının etkinliği istatistiksel ölçüm yöntemleri olan Normalize Mutlak Ortalama Hata (NMAE), Normalize Ortalama Hata Kareleri Kökü (NRMSE) ve determinasyon katsayısı (𝐑^𝟐) ile ölçülmüştür. Sonuçlara göre bayes algoritması ile optimize edilmiş karar ağacı ile oluşturulan torbalama yöntemi yıllık ortalama NRMSE, NMAE, 𝐑^𝟐 kriterleri sırasıyla 11.045 %, 4.880 %, 0.899 değerlerini almış ve hem yıllık hem de mevsimlik sonuçlar açısından en iyi performansa sahip model seçilmiştir.

Keywords

Yenilenebilir enerji, Rüzgar gücü tahmini, Bayes Optimizasyonu, Topluluk öğrenmesi

Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

Abstract

In wind energy studies, predicting the short-term energy generation amount for wind power plants and determining the production offer to be placed on the market play an important role. In this study an hourly short-term wind power estimation of a wind turbine located in Turkey with an installed power of 3600 kW has been made. Estimation results were evaluated on a seasonal and annual basis. New hybrid models have been developed for short-term wind power prediction, consisting of Bayesian Optimization (BO), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Decision Tree (DT), stacking, and bagging algorithms. In the proposed prediction approach, it is aimed to reduce prediction errors by combining different regression algorithms with the BO method and ensemble algorithms. Unlike other wind prediction studies, BO was used for the first time in the hyperparameter selection of the regression algorithms selected as the basic learner in the study. Bayesian optimized decision tree (BO-DT) with the lowest error values among the base learners, and Bayesian optimized gaussian process regression (BO-GPR) combined with bagging and stacking. The efficiency of ensemble learning algorithms was measured by the statistical measurement methods Normalized Absolute Mean Error (NMAE), Normalized Root of Mean Squares Error (NRMSE), and determination coefficient (R²). According to the results, the bagging method created with the BO-DT took the annual average NRMSE, NMAE, R² criteria of 11.045%, 4.880%, 0.899, respectively, and the model with the best performance was selected in terms of both annual and seasonal results.

Keywords

Bayesian Optimization, Ensemble Learning, Renewable energy, Wind power prediction

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