Multi-Step Wind Speed Forecasting using Transformer

Accurate wind speed forecasting is critical for optimizing wind energy generation, grid integration, and disaster preparedness.Traditional forecasting methods often struggle with capturing complex temporal dependencies in wind speed variations.This study proposes a multi-step wind speed forecasting model using Transformer-based deep learning architecture.The model leverages self-attention mechanisms to effectively capture long-range dependencies in wind speed time series data.We evaluate the model's performance on real-world datasets and compare it with conventional forecasting approaches such as ARIMA, LSTM, and GRU.Experimental results demonstrate that the Transformer model achieves superior forecasting accuracy, particularly for longer prediction horizons.Additionally, we analyze the impact of hyperparameter tuning and input sequence length on forecast reliability.The findings suggest that Transformer-based forecasting can significantly enhance wind speed prediction, making it a promising tool for wind energy applications and meteorological forecasting.Future work will explore hybrid approaches integrating domain knowledge with deep learning to further improve forecasting accuracy.The proposed method has the potential to contribute to sustainable energy planning and climate resilience strategies.