Recurrent Neural Networks Based Wind Speed Forecasting Models: A Case Study of Yalova

Year 2022, Volume: 5 Issue: 2, 178 - 188, 21.09.2022

Zeliha Nur Kiriş , Ömer Faruk Beyca , Fuat Kosanoğlu

https://doi.org/10.38016/jista.1120383

Cited By: 2

Abstract

Global warming and other adversarial effects caused by fossil fuel sources, renewable energy sources have been attracted more than ever. Especially, parties of Paris Climate Agreement countries pledge to reduce greenhouse gas emissions. Among renewable energy sources, wind energy is one of the significant and eligible source to produce energy sustainably. Wind energy is also one of the most important renewable energy source due to Turkey’s notable wind energy potential. Although wind energy is one of the most important clean energy sources, there are several challenges, such as intermittent and uncertain nature of wind places. Therefore, efficient and reliable energy planning and distribution mostly rely on prediction of wind energy with high accuracy. In this study, we propose four Reccurent Neural Network (RNN) methods to predict short-term wind energy production. We utilize data obtained from a station located in Yalova, Turkey to assess the performance of proposed algorithms. In our analysis, we plan to improve maintenance planning and intervene the sudden breakdowns by predicting 1 hour ahead energy production. First, we analyze the data received from the station, and the data sets were made suitable for the models. The performance results obtained from the models are plausible. Our results indicate that RNN methods can be successfully used to predict wind speed.

Keywords

wind speed forecasting, ARIMA, recurrent neural networks

Tekrarlayan Sinir Ağları Temelli Rüzgâr Hızı Tahmin Modelleri: Yalova Bölgesinde Bir Uygulama

Abstract

Küresel ısınma ve fosil yakıtların çevreye verdiği zararlardan dolayı son yıllarda yenilenebilir enerji kaynakları büyük önem kazanmıştır. Özellikle Paris İklim Anlaşmasıyla ülkeler çevreye zararlı gaz salınımını azaltmak konusunda taahhütlerde bulunmuşlardır. Günümüzde en önemli yenilenebilir enerji kaynaklarından biri de rüzgâr enerjisidir. Türkiye’nin sahip olduğu rüzgâr potansiyeli düşünüldüğünde enerji üretiminde rüzgâr enerjisi daha da önem kazanmaktadır. Rüzgâr enerjisi temiz bir enerji kaynağı olmasına rağmen rüzgârın değişken bir kaynak olması nedeniyle üretilen enerjinin verimli kullanılıp dağıtılabilmesi ve planlama yapılabilmesi sağlıklı rüzgâr enerjisi üretim tahminlerine dayanmaktadır. Bu çalışmada, dört farklı Tekrarlayan Sinir Ağları (TSA) modeli rüzgâr enerjisi üretim tahminlemesi için kullanılmıştır. Çalışmada, Türkiye’nin Yalova ilinde bulunan bir istasyondan elde edilen veriler kullanılarak kısa süreli rüzgâr hızı tahmini yapılmıştır. Analizde bir saat sonrasını tahmin ederek oluşacak ani arıza ve bakım planlamalarına müdahale edilmesi amaçlanmıştır. Öncelikle istasyondan alınan veriler incelenmiş, veri analizleri yapılmış, var olan verilerden yeni veriler üretilmiş ve veri setleri modeller için uygun hale getirilmiştir. Modellerden elde edilen performans sonuçları kabul edilebilir aralıkta olup TSA yöntemlerinin rüzgâr hızı tahmininde başarılı bir şekilde kullanılabileceğini, ve geleneksel zaman serisi yöntemlerine göre daha iyi sonuçlar verdiği sonucuna varılmıştır.

Keywords

rüzgâr hızı tahmini, rüzgâr enerjisi, ARIMA, tekrarlayan sinir ağları

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