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Journals / Sakarya University Journal of Science / 2026 / Volume: 22 Issue: 1

Short-term multi-step wind speed prediction using statistical methods and artificial neural networks

İstatistiksel metotlar ve yapay sinir ağları kullanarak kısa dönem çok adımlı rüzgâr hızı tahmini

İsmail Kırbaş(MEHMET AKİF ERSOY ÜNİVERSİTESİ, MÜHENDİSLİK-MİMARLIK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-1206-8294
Pages
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Publication Date
2018-02-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

While the wind speed estimation studies in the literature generally focus only on single step prediction success, detailed evaluation of commonly used estimation methods at the prospective 12 step level has been carried out.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The results of the observations made by TUBITAK T60 national observation house meteorological station in April, 2016 were compiled on this website using the PHP programming language.
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Key Findings & Data:There is a significant difference in error rates between the ARIMA models and the artificial neural networks examined as a result of comparisons with the calculated calculations and actual data.
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Conclusion & Impact:While the wind speed estimation studies in the literature generally focus only on single step prediction success, detailed evaluation of commonly used estimation methods at the prospective 12 step level has been carried out.
🔬Design:Modeling & Simulation
📍Scope:2016 Verileri
⚙️Variables:Wind speed prediction · ARIMA · NAR

Abstract

The results of the observations made by TUBITAK T60 national observation house meteorological station in April, 2016 were compiled on this website using the PHP programming language. Obtained wind speed data were analysed using statistical and artificial neural network methods and predicted wind speed predictions over the time series brought to the field. There is a significant difference in error rates between the ARIMA models and the artificial neural networks examined as a result of comparisons with the calculated calculations and actual data. While the wind speed estimation studies in the literature generally focus only on single step prediction success, detailed evaluation of commonly used estimation methods at the prospective 12 step level has been carried out.

Özet

Bu çalışmada TÜBİTAK T60 ulusal gözlem evi meteoroloji istasyonunun 2016 yılı nisan ayı içerisinde yaptığı gözlem sonuçları PHP programlama dili kullanılarak web sitesi üzerinden derlenmiştir. Elde edilen rüzgâr hızı verileri istatistiksel ve yapay sinir ağı metotları kullanılarak incelenmiş ve meydana getirilen zaman serisi üzerinden ileriye yönelik rüzgâr hızı kestirimlerinde bulunulmuştur. Yapılan hesaplamalar ve gerçek veriler ile kıyaslamalar sonucunda incelenen ARIMA modelleri ve yapay sinir ağları arasında belirgin bir hata oranı farkı görülmüştür. Literatürde yer alan rüzgâr hızı tahmin çalışmaları genellikle sadece tek adım tahmin başarısı üzerinde yoğunlaşırken, önerilen çalışmada sık kullanılan tahmin metotlarının ileriye dönük 12 adım seviyesinde ayrıntılı değerlendirilmeleri gerçekleştirilmiştir.

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Cite This Article

Kırbaş, İ. (2018). Short-term multi-step wind speed prediction using statistical methods and artificial neural networks. Sakarya University Journal of Science, Volume: 22 Issue: 1. https://doi.org/10.16984/saufenbilder.305224

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