Shape-Aware Seq2Seq Model for Accurate Multistep Wind Speed Forecasting

Wind speed is a crucial parameter affecting wind energy utilization. However, its volatility leads to time-varying power output. Herein, a novel Seq2Seq model integrating deep learning, data denoising, and a shape-aware loss function is proposed for accurate multistep wind speed forecasting. In this model, the wind speed data is first denoised using the maximal overlap discrete wavelet transform. Next, an encoder-decoder network based on a temporal convolutional network, bidirectional gated recurrent unit, and multihead self-attention is employed for forecasting. Additionally, to enhance the ability of the model to identify temporal dynamics, a shape-aware loss function, ITILDE-Q, is employed in the model. To verify the effectiveness of the proposed model, a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds. Three error metrics and a similarity metric were adopted for comprehensive evaluation. The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios, with particularly pronounced differences in performance over longer forecast horizons. Furthermore, the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.