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Dergiler / Journal of AI / 2025 / Volume: 9 Issue: 1

Forecasting Wind Speed with Autoregressive and Long-Short Term Memory Neural Network Models

Serkan Ansay(IZMIR BAKIRCAY UNIVERSITY)0000-0002-3368-3886
Bayram Köse(IZMIR BAKIRCAY UNIVERSITY)0000-0003-0256-5921
İbrahim Işıklı(IZMIR BAKIRCAY UNIVERSITY)0000-0002-0778-7163
Ceyda Mülayim(IZMIR BAKIRCAY UNIVERSITY)0009-0005-3780-7486
Bekir Ertilav(IZMIR BAKIRCAY UNIVERSITY)0009-0003-5027-5061
Sayfa
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Yayın Tarihi
2025-12-31
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The findings contribute to the development of intelligent forecasting systems for efficient renewable energy management.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The growing need for sustainable and efficient energy systems has intensified interest in accurate renewable energy forecasting.
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Temel Bulgular & Veri:This study investigates and compares the performance of three different approaches for wind speed prediction: Autoregressive (AR), Long Short-Term Memory (LSTM) neural networks, and a hybrid AR–LSTM model.
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Sonuç & Katkı:The findings contribute to the development of intelligent forecasting systems for efficient renewable energy management.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Autoregressive · Artificial neural networks · Long-short term memory

Abstract

The growing need for sustainable and efficient energy systems has intensified interest in accurate renewable energy forecasting. In particular, wind speed forecasting is vital for the reliable integration of wind energy into power systems. This study investigates and compares the performance of three different approaches for wind speed prediction: Autoregressive (AR), Long Short-Term Memory (LSTM) neural networks, and a hybrid AR–LSTM model. Real wind speed data collected from İzmir, Turkey, were used in the experiments. The AR model, a linear statistical method, was evaluated alongside the LSTM model, a deep learning method capable of capturing long-term temporal dependencies. A hybrid model was also developed to benefit from the strengths of both. Additionally, noise reduction techniques such as Moving Average and Gaussian Filtering were applied to enhance data quality and model accuracy. The results demonstrated that the LSTM model achieved the lowest RMSE value (0.084), outperforming both the AR and hybrid models. This suggests that LSTM-based models are more suitable for capturing complex and nonlinear patterns in wind speed data. The findings contribute to the development of intelligent forecasting systems for efficient renewable energy management.

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Bu Makaleye Atıf Yap

Ansay, S., Köse, B., Işıklı, İ., Mülayim, C., Ertilav, B. (2025). Forecasting Wind Speed with Autoregressive and Long-Short Term Memory Neural Network Models. Journal of AI, Volume: 9 Issue: 1. https://doi.org/10.61969/jai.1789834

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