关键词: 风电功率预测, 神经网络模型, 贝叶斯优化, 特征融合, 深度学习

Abstract: To enhance the accuracy of short-term wind power prediction, a extend Long Short-Term Memory (xLSTM)-Transformer model based on Bayesian Optimization (BO) and Feature Fusion (FF) was proposed. The proposed model integrates the temporal processing capability of Long Short-Term Memory (LSTM) network with the dynamic feature fusion ability of Transformer’s self-attention mechanism. By employing BO method, the hyperparameters of the model were efficiently optimized in a fewer iteration, significantly reducing the computational resource requirements. Experimental results show that, compared with single LSTM, Transformer model, Gated Recurrent Unit (GRU) model, and xLSTM-Transformer model without FF and BO, the proposed model performs better in key predictive indicators such as coefficient of determination R², Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Key words: wind power prediction, neural network model, Bayesian Optimization (BO), feature fusion, deep learning