Ampirik Kip Ayrıştırma Yöntemi ile Elde Edilen İçsel Kip Fonksiyonlarının Derin Öğrenme Tabanlı Rüzgâr Hızı Tahmin Modeli Başarımına Etkisinin Belirlenmesi

Year 2021, Issue: 31, 661 - 669, 31.12.2021

Caner Barış Ahmed Cemil Bilgin Aytaç Altan

https://doi.org/10.31590/ejosat.1026742

Abstract

Son yıllarda küresel anlamda etkisini yoğun şekilde gösteren iklim değişikliğinin temelinde fosil yakıt tüketimi kaynaklı sera etkisinin kuvvetlenmesi yer aldığı bilinmektedir. İklim değişikliği neticesinde canlılar için hayati önem taşıyan su kaynaklarının azalacağı, ekolojik dengenin bozularak çölleşme ve kuraklığın artacağı öngörülmektedir. Bu sorunla başa çıkılabilmesi için fosil yakıt tüketiminin azaltılması ve enerji ihtiyacının yenilenebilir enerji kaynakları ile karşılanması gerekmektedir. Bu nedenle, temiz ve yenilenebilir bir enerji türü olan rüzgâr enerjisine olan ilgi dünya çapında her geçen gün artmaktadır. Bununla birlikte, rüzgâr hızının güçlü rastgeleliği ve durağan olmaması rüzgâr gücünün elektrik şebekesine entegre edilmesini zorlaştırmaktadır. Bu zorlukların üstesinden gelebilmek için rüzgâr hızının güvenilir ve yüksek doğrulukla tahmin edilmesi kritik önem arz etmektedir. Bu çalışmada, doğrusal olmayan dinamiklere sahip rüzgâr hızının yüksek doğrulukla tahmin edilebilmesi için ampirik kip ayrıştırma ve derin öğrenme yöntemlerinden uzun-kısa süreli bellek tekniklerini içeren melez modeldeki içsel kip fonksiyonlarının rüzgâr hızı tahmin performansı üzerindeki etkileri incelenmektedir. Türkiye’nin en yüksek rüzgâr enerji potansiyeline sahip bölgeleri arasında yer alan Marmara bölgesindeki Bandırma meteoroloji istasyonundan toplanan rüzgâr hızı verileri ampirik kip ayrıştırma tekniği ile içsel kip fonksiyonlarına ayrıştırılmaktadır. Her bir içsel kip fonksiyonunun tahmin modeli üzerindeki başarımının belirlenebilmesi için sırasıyla her bir içsel kip fonksiyonu derin öğrenme modeline dahil edilmeden tahmin modellerinin performansları ölçülmektedir. Tahmin modellerinin başarımları istatistiksel performans metriklerine göre hesaplanmaktadır.

Keywords

Ampirik kip ayrıştırma, İçsel kip fonksiyonu, Rüzgâr hızı tahmini, Derin öğrenme, Uzun-kısa süreli bellek.

Determining the Effect of Intrinsic Mode Functions Obtained by the Empirical Mode Decomposition on the Performance of Deep Learning based Wind Speed Prediction Model

Abstract

In recent years, it is known that the strengthening of the greenhouse effect caused by fossil fuel consumption is at the root of climate change, which has had an intense impact on the global scale. As a result of climate change, it is predicted that water resources, which are vital for living things, will decrease, and the ecological balance will deteriorate and desertification and drought will increase. In order to cope with this problem, fossil fuel consumption should be reduced and energy needs should be met with renewable energy sources. Therefore, the interest in wind energy, which is a clean and renewable energy type, is increasing day by day around the world. However, the strong randomness and non-stationarity of the wind speed make it difficult to integrate wind power into the electricity grid. To overcome these challenges, reliable and highly accurate estimation of wind speed is critical. In this study, the effects of intrinsic mode functions in the hybrid model, which includes empirical mode decomposition and long-short-term memory (LSTM) techniques, on wind speed prediction performance are investigated for high accuracy prediction of wind speed, which has nonlinear dynamics. Wind speed data collected from Bandırma meteorology station in the Marmara region, which is among the regions with the highest wind energy potential in Turkey, are decomposed to intrinsic mode functions by empirical mode decomposition technique. To determine the performance of each intrinsic mode function on the wind speed prediction model, the performances of the prediction models are measured without including each intrinsic mode function in the deep learning model, respectively. The performance of prediction models is computed according to statistical performance metrics.

Keywords

Empirical mode decomposition, Intrinsic mode function, Wind speed prediction, Deep learning, Long-short term memory (LSTM)

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