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

Authors : Serkan Ansay, Bayram Köse, İbrahim Işıklı, Ceyda Mülayim, Bekir Ertilav

Pages : 98-121

Doi:10.61969/jai.1789834

View : 141 | Download : 48

Publication Date : 2025-12-31

Article Type : Research Paper

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.
Keywords : Autoregressive, Artificial neural networks, Long-short term memory, Wind energy, Modeling of energy systems

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