A Predictive Framework for Hourly Wind Speed Forecasting Using Stacked Recurrent Neural Networks
DOI:
https://doi.org/10.26438/ijcse/v13i10.18Keywords:
Wind speed forecasting, Deep learning, recurrent neural networks, Gated Recurrent Unit, Long Short-Term Memory Networks, Mean Absolute errorAbstract
The growing demand for low-cost, eco-friendly energy has established wind power as a pivotal renewable source, making accurate wind speed forecasting critical. The study introduces a deep learning framework that integrates stacked Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks to predict hourly wind speed. The model’s performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²), and benchmarked against Support Vector Regression (SVR) and Artificial Neural Network (ANN) models. Experimental findings reveal that the proposed stacked GRU and LSTM models consistently surpass the comparative methods, highlighting its robustness and effectiveness in wind speed forecasting.
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