Improved BAS-BiLSTM-Attention model for wind power forecasting

Abstract To optimize the complex nonlinear temporal relationships in wind power forecasting, we proposed an improved BAS-BiLSTM-Attention model for enhancing the accuracy and robustness of wind power predictions. The Beetle Antennae Search algorithm was utilized for obtaining hidden features across different frequency bands. The Bidirectional Long Short-Term Memory algorithm captured the bidirectional temporal associations of hidden features. Finally, an Attention mechanism was added to ensure that the weight selection of the feature components remains unaffected. Results demonstrated that our method performed excellently in wind power forecasting tasks. Compared to individual models and other common algorithms, it exhibited higher prediction accuracy and robustness across multiple evaluation metrics.