Wind Speed Prediction Using Deep Recurrent Neural Networks and Farm Platform Features for One-Hour-Ahead Forecast

Year 2024, Volume: 39 Issue: 2, 287 - 300, 11.07.2024

Emre Özbilge , Yonal Kırsal

https://doi.org/10.21605/cukurovaumfd.1513981

Abstract

This paper proposes a deep recurrent neural network (DRNN) approach to model the one-hour-ahead wind speed forecasting by using various meteorological sensory data from the North Wyke farm platform (NWFP). To refine model input, mutual information analysis is applied to eliminate irrelevant sensory data. The DRNN architecture employs three recurrent layers Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and simple Recurrent Neural Network (RNN) to capture temporal relationships. The proposed networks are tested using real-life, one-year data from the NWFP. The results showed a strong correlation between the actual and predicted wind speed for LSTM, GRU, and RNN layers-based DRNN, however, simple RNN slightly outperformed the other two recurrent layers. The distribution of the network errors over the year is also analyzed. Although the observed meteorological data between the years was from different distributions, the proposed network generalized well even though these data were altered due to global warming.

Keywords

Wind speed prediction, Deep recurrent neural network, Intelligent systems, Time-series prediction, Feature selection

Derin Tekrarlayan Sinir Ağları ve Çiftlik Platformu Özellikleri Kullanılarak Bir Saat Önceden Rüzgâr Hızı Tahmini

Abstract

Bu makale, Kuzey Wyke çiftliği platformundan (NWFP) çeşitli meteorolojik veriler kullanarak bir saat öncesine yönelik rüzgâr hızı tahmini modellemek için derin tekrarlı sinir ağı (DRNN) yaklaşımını önermektedir. Model girişini iyileştirmek için karşılıklı bilgi analizi kullanılarak ilgisi olmayan veriler elenmiştir. DRNN mimarisi, zamansal ilişkileri yakalamak üzere üç tekrarlı katmanı içerir: Uzun Kısa Vadeli Bellek (LSTM), Kapılı Tekrarlı Birim (GRU) ve basit Tekrarlı Sinir Ağı (RNN). Önerilen ağlar, NWFP'den gerçek zamanlı, bir yıllık veri kullanılarak test edilmiştir. Sonuçlar, LSTM, GRU ve basit RNN katmanları temelli DRNN için gerçek ve tahmin edilen rüzgâr hızı arasında güçlü bir korelasyon olduğunu göstermiştir; ancak basit RNN, diğer iki tekrarlı katmandan biraz daha iyi performans sergilemiştir. Ayrıca, ağ hatalarının yıl boyunca dağılımı analiz edilmiştir. Gözlemlenen meteorolojik verilerin yıllar arasında farklı dağılımlardan olmasına rağmen, önerilen ağ, bu veriler küresel ısınma nedeniyle değişmiş olsa bile iyi genelleme yapmıştır.

Keywords

Rüzgâr hızı tahmini, Derin tekrarlayan sinir ağı, Akıllı sistemler, Zaman serisi tahmini, Özellik seçimi

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